text stringlengths 56 1.16k |
|---|
[2023-10-24 00:24:41,292::train::INFO] [train] Iter 577085 | loss 0.8892 | loss(rot) 0.4484 | loss(pos) 0.1434 | loss(seq) 0.2974 | grad 2.5589 | lr 0.0000 | time_forward 3.3770 | time_backward 4.9300 |
[2023-10-24 00:24:44,688::train::INFO] [train] Iter 577086 | loss 0.9800 | loss(rot) 0.6354 | loss(pos) 0.0777 | loss(seq) 0.2669 | grad 5.5103 | lr 0.0000 | time_forward 1.4700 | time_backward 1.9230 |
[2023-10-24 00:24:47,350::train::INFO] [train] Iter 577087 | loss 0.8264 | loss(rot) 0.7992 | loss(pos) 0.0222 | loss(seq) 0.0049 | grad 3.2961 | lr 0.0000 | time_forward 1.2460 | time_backward 1.4120 |
[2023-10-24 00:24:56,253::train::INFO] [train] Iter 577088 | loss 0.1666 | loss(rot) 0.1453 | loss(pos) 0.0213 | loss(seq) 0.0000 | grad 2.1680 | lr 0.0000 | time_forward 3.6160 | time_backward 5.2840 |
[2023-10-24 00:25:05,310::train::INFO] [train] Iter 577089 | loss 0.3899 | loss(rot) 0.2135 | loss(pos) 0.0171 | loss(seq) 0.1593 | grad 2.0951 | lr 0.0000 | time_forward 3.7200 | time_backward 5.3330 |
[2023-10-24 00:25:14,222::train::INFO] [train] Iter 577090 | loss 0.7820 | loss(rot) 0.4102 | loss(pos) 0.0871 | loss(seq) 0.2848 | grad 2.3833 | lr 0.0000 | time_forward 3.8360 | time_backward 5.0730 |
[2023-10-24 00:25:16,861::train::INFO] [train] Iter 577091 | loss 0.2550 | loss(rot) 0.1573 | loss(pos) 0.0191 | loss(seq) 0.0786 | grad 3.0951 | lr 0.0000 | time_forward 1.2700 | time_backward 1.3660 |
[2023-10-24 00:25:19,091::train::INFO] [train] Iter 577092 | loss 0.4835 | loss(rot) 0.1445 | loss(pos) 0.0383 | loss(seq) 0.3006 | grad 2.9843 | lr 0.0000 | time_forward 1.0250 | time_backward 1.1740 |
[2023-10-24 00:25:25,764::train::INFO] [train] Iter 577093 | loss 1.1562 | loss(rot) 1.0600 | loss(pos) 0.0251 | loss(seq) 0.0711 | grad 3.5718 | lr 0.0000 | time_forward 2.8700 | time_backward 3.8000 |
[2023-10-24 00:25:32,331::train::INFO] [train] Iter 577094 | loss 0.5691 | loss(rot) 0.1301 | loss(pos) 0.4166 | loss(seq) 0.0224 | grad 3.1284 | lr 0.0000 | time_forward 2.6630 | time_backward 3.9010 |
[2023-10-24 00:25:39,440::train::INFO] [train] Iter 577095 | loss 0.1989 | loss(rot) 0.1149 | loss(pos) 0.0164 | loss(seq) 0.0677 | grad 2.3049 | lr 0.0000 | time_forward 3.1250 | time_backward 3.9820 |
[2023-10-24 00:25:41,656::train::INFO] [train] Iter 577096 | loss 0.4453 | loss(rot) 0.2811 | loss(pos) 0.0472 | loss(seq) 0.1171 | grad 1.9862 | lr 0.0000 | time_forward 1.0090 | time_backward 1.2040 |
[2023-10-24 00:25:48,065::train::INFO] [train] Iter 577097 | loss 0.6180 | loss(rot) 0.2581 | loss(pos) 0.0562 | loss(seq) 0.3038 | grad 10.3725 | lr 0.0000 | time_forward 2.7200 | time_backward 3.6790 |
[2023-10-24 00:25:57,885::train::INFO] [train] Iter 577098 | loss 1.8085 | loss(rot) 1.6253 | loss(pos) 0.0325 | loss(seq) 0.1507 | grad 6.5781 | lr 0.0000 | time_forward 3.7010 | time_backward 6.1170 |
[2023-10-24 00:26:06,254::train::INFO] [train] Iter 577099 | loss 0.4531 | loss(rot) 0.1428 | loss(pos) 0.2652 | loss(seq) 0.0452 | grad 3.8657 | lr 0.0000 | time_forward 3.8520 | time_backward 4.5140 |
[2023-10-24 00:26:15,166::train::INFO] [train] Iter 577100 | loss 0.1960 | loss(rot) 0.0509 | loss(pos) 0.0224 | loss(seq) 0.1227 | grad 1.5143 | lr 0.0000 | time_forward 3.6550 | time_backward 5.2530 |
[2023-10-24 00:26:23,905::train::INFO] [train] Iter 577101 | loss 0.6538 | loss(rot) 0.2454 | loss(pos) 0.0688 | loss(seq) 0.3397 | grad 3.5014 | lr 0.0000 | time_forward 3.6130 | time_backward 5.1230 |
[2023-10-24 00:26:33,498::train::INFO] [train] Iter 577102 | loss 0.5327 | loss(rot) 0.0167 | loss(pos) 0.5141 | loss(seq) 0.0018 | grad 10.1621 | lr 0.0000 | time_forward 4.2860 | time_backward 5.3040 |
[2023-10-24 00:26:36,277::train::INFO] [train] Iter 577103 | loss 1.6290 | loss(rot) 0.7005 | loss(pos) 0.2246 | loss(seq) 0.7039 | grad 6.8399 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4430 |
[2023-10-24 00:26:43,284::train::INFO] [train] Iter 577104 | loss 3.3402 | loss(rot) 0.0143 | loss(pos) 3.3259 | loss(seq) 0.0000 | grad 18.9964 | lr 0.0000 | time_forward 2.9850 | time_backward 3.9830 |
[2023-10-24 00:26:52,502::train::INFO] [train] Iter 577105 | loss 0.5598 | loss(rot) 0.3631 | loss(pos) 0.0201 | loss(seq) 0.1767 | grad 57.9779 | lr 0.0000 | time_forward 3.9310 | time_backward 5.2850 |
[2023-10-24 00:26:55,391::train::INFO] [train] Iter 577106 | loss 0.4796 | loss(rot) 0.1336 | loss(pos) 0.0520 | loss(seq) 0.2940 | grad 2.3245 | lr 0.0000 | time_forward 1.4040 | time_backward 1.4820 |
[2023-10-24 00:26:58,224::train::INFO] [train] Iter 577107 | loss 0.3357 | loss(rot) 0.0654 | loss(pos) 0.2448 | loss(seq) 0.0256 | grad 2.8374 | lr 0.0000 | time_forward 1.3650 | time_backward 1.4650 |
[2023-10-24 00:27:06,130::train::INFO] [train] Iter 577108 | loss 1.6554 | loss(rot) 1.0071 | loss(pos) 0.1252 | loss(seq) 0.5230 | grad 6.9252 | lr 0.0000 | time_forward 3.4170 | time_backward 4.4850 |
[2023-10-24 00:27:15,660::train::INFO] [train] Iter 577109 | loss 0.7216 | loss(rot) 0.3946 | loss(pos) 0.0719 | loss(seq) 0.2551 | grad 2.4977 | lr 0.0000 | time_forward 3.5940 | time_backward 5.9320 |
[2023-10-24 00:27:18,643::train::INFO] [train] Iter 577110 | loss 0.1102 | loss(rot) 0.0727 | loss(pos) 0.0188 | loss(seq) 0.0188 | grad 1.6418 | lr 0.0000 | time_forward 1.5850 | time_backward 1.3960 |
[2023-10-24 00:27:27,177::train::INFO] [train] Iter 577111 | loss 0.4845 | loss(rot) 0.2247 | loss(pos) 0.0605 | loss(seq) 0.1993 | grad 3.0185 | lr 0.0000 | time_forward 3.6060 | time_backward 4.9250 |
[2023-10-24 00:27:34,312::train::INFO] [train] Iter 577112 | loss 0.7061 | loss(rot) 0.5927 | loss(pos) 0.0459 | loss(seq) 0.0675 | grad 4.8704 | lr 0.0000 | time_forward 2.8610 | time_backward 4.2700 |
[2023-10-24 00:27:41,722::train::INFO] [train] Iter 577113 | loss 0.6741 | loss(rot) 0.3812 | loss(pos) 0.0303 | loss(seq) 0.2626 | grad 4.2511 | lr 0.0000 | time_forward 2.8430 | time_backward 4.5640 |
[2023-10-24 00:27:50,859::train::INFO] [train] Iter 577114 | loss 0.9510 | loss(rot) 0.3428 | loss(pos) 0.1544 | loss(seq) 0.4538 | grad 3.6824 | lr 0.0000 | time_forward 3.7900 | time_backward 5.3450 |
[2023-10-24 00:27:53,606::train::INFO] [train] Iter 577115 | loss 0.3723 | loss(rot) 0.1844 | loss(pos) 0.1022 | loss(seq) 0.0857 | grad 2.8779 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4100 |
[2023-10-24 00:27:59,131::train::INFO] [train] Iter 577116 | loss 0.8037 | loss(rot) 0.6101 | loss(pos) 0.0163 | loss(seq) 0.1774 | grad 3.5891 | lr 0.0000 | time_forward 2.4000 | time_backward 3.1210 |
[2023-10-24 00:28:01,841::train::INFO] [train] Iter 577117 | loss 0.4286 | loss(rot) 0.3966 | loss(pos) 0.0320 | loss(seq) 0.0000 | grad 1.8272 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4200 |
[2023-10-24 00:28:06,921::train::INFO] [train] Iter 577118 | loss 0.8479 | loss(rot) 0.1273 | loss(pos) 0.1308 | loss(seq) 0.5898 | grad 3.0642 | lr 0.0000 | time_forward 2.2410 | time_backward 2.8360 |
[2023-10-24 00:28:09,555::train::INFO] [train] Iter 577119 | loss 0.7602 | loss(rot) 0.0373 | loss(pos) 0.6588 | loss(seq) 0.0642 | grad 8.4118 | lr 0.0000 | time_forward 1.2650 | time_backward 1.3550 |
[2023-10-24 00:28:10,117::train::INFO] [train] Iter 577120 | loss 2.0762 | loss(rot) 1.9557 | loss(pos) 0.1205 | loss(seq) 0.0000 | grad 15.8596 | lr 0.0000 | time_forward 0.2440 | time_backward 0.3160 |
[2023-10-24 00:28:16,085::train::INFO] [train] Iter 577121 | loss 0.2746 | loss(rot) 0.2287 | loss(pos) 0.0321 | loss(seq) 0.0138 | grad 3.1988 | lr 0.0000 | time_forward 2.5920 | time_backward 3.3720 |
[2023-10-24 00:28:22,980::train::INFO] [train] Iter 577122 | loss 0.5175 | loss(rot) 0.2693 | loss(pos) 0.0551 | loss(seq) 0.1931 | grad 3.3713 | lr 0.0000 | time_forward 2.9660 | time_backward 3.9260 |
[2023-10-24 00:28:29,880::train::INFO] [train] Iter 577123 | loss 1.1329 | loss(rot) 0.7969 | loss(pos) 0.0602 | loss(seq) 0.2757 | grad 26.6963 | lr 0.0000 | time_forward 2.8410 | time_backward 4.0560 |
[2023-10-24 00:28:36,163::train::INFO] [train] Iter 577124 | loss 1.8932 | loss(rot) 0.2895 | loss(pos) 1.5995 | loss(seq) 0.0041 | grad 12.3826 | lr 0.0000 | time_forward 2.6810 | time_backward 3.5990 |
[2023-10-24 00:28:45,528::train::INFO] [train] Iter 577125 | loss 0.6197 | loss(rot) 0.4775 | loss(pos) 0.0544 | loss(seq) 0.0879 | grad 2.7581 | lr 0.0000 | time_forward 4.0420 | time_backward 5.3210 |
[2023-10-24 00:28:52,673::train::INFO] [train] Iter 577126 | loss 0.5057 | loss(rot) 0.3490 | loss(pos) 0.0285 | loss(seq) 0.1282 | grad 2.8777 | lr 0.0000 | time_forward 3.1750 | time_backward 3.9660 |
[2023-10-24 00:28:59,592::train::INFO] [train] Iter 577127 | loss 0.2021 | loss(rot) 0.0596 | loss(pos) 0.0647 | loss(seq) 0.0777 | grad 2.6781 | lr 0.0000 | time_forward 3.0130 | time_backward 3.9020 |
[2023-10-24 00:29:06,457::train::INFO] [train] Iter 577128 | loss 0.2244 | loss(rot) 0.0476 | loss(pos) 0.0270 | loss(seq) 0.1498 | grad 2.4369 | lr 0.0000 | time_forward 2.8910 | time_backward 3.9710 |
[2023-10-24 00:29:22,008::train::INFO] [train] Iter 577129 | loss 0.4213 | loss(rot) 0.0220 | loss(pos) 0.3972 | loss(seq) 0.0021 | grad 5.3693 | lr 0.0000 | time_forward 10.3390 | time_backward 5.2080 |
[2023-10-24 00:29:24,655::train::INFO] [train] Iter 577130 | loss 1.0493 | loss(rot) 0.0041 | loss(pos) 1.0448 | loss(seq) 0.0003 | grad 21.9161 | lr 0.0000 | time_forward 1.2550 | time_backward 1.3890 |
[2023-10-24 00:29:32,045::train::INFO] [train] Iter 577131 | loss 0.1754 | loss(rot) 0.1113 | loss(pos) 0.0346 | loss(seq) 0.0294 | grad 1.8525 | lr 0.0000 | time_forward 3.1170 | time_backward 4.2700 |
[2023-10-24 00:29:39,584::train::INFO] [train] Iter 577132 | loss 1.2627 | loss(rot) 1.0615 | loss(pos) 0.0524 | loss(seq) 0.1489 | grad 12.9463 | lr 0.0000 | time_forward 3.3300 | time_backward 4.2070 |
[2023-10-24 00:29:47,881::train::INFO] [train] Iter 577133 | loss 0.6387 | loss(rot) 0.3899 | loss(pos) 0.0213 | loss(seq) 0.2275 | grad 5.9706 | lr 0.0000 | time_forward 3.0880 | time_backward 5.2050 |
[2023-10-24 00:29:57,196::train::INFO] [train] Iter 577134 | loss 0.2069 | loss(rot) 0.1842 | loss(pos) 0.0214 | loss(seq) 0.0013 | grad 2.3456 | lr 0.0000 | time_forward 3.8850 | time_backward 5.4280 |
[2023-10-24 00:30:05,383::train::INFO] [train] Iter 577135 | loss 0.3348 | loss(rot) 0.2952 | loss(pos) 0.0396 | loss(seq) 0.0000 | grad 18.4902 | lr 0.0000 | time_forward 3.4840 | time_backward 4.7000 |
[2023-10-24 00:30:13,890::train::INFO] [train] Iter 577136 | loss 1.0147 | loss(rot) 0.5774 | loss(pos) 0.1453 | loss(seq) 0.2920 | grad 4.5074 | lr 0.0000 | time_forward 3.5910 | time_backward 4.9120 |
[2023-10-24 00:30:21,524::train::INFO] [train] Iter 577137 | loss 0.2759 | loss(rot) 0.1615 | loss(pos) 0.0251 | loss(seq) 0.0892 | grad 1.5751 | lr 0.0000 | time_forward 3.2430 | time_backward 4.3880 |
[2023-10-24 00:30:29,927::train::INFO] [train] Iter 577138 | loss 0.4696 | loss(rot) 0.3163 | loss(pos) 0.0146 | loss(seq) 0.1388 | grad 3.3198 | lr 0.0000 | time_forward 3.6550 | time_backward 4.7450 |
[2023-10-24 00:30:37,623::train::INFO] [train] Iter 577139 | loss 0.6939 | loss(rot) 0.1851 | loss(pos) 0.3621 | loss(seq) 0.1467 | grad 2.9468 | lr 0.0000 | time_forward 3.3800 | time_backward 4.3120 |
[2023-10-24 00:30:40,215::train::INFO] [train] Iter 577140 | loss 0.3954 | loss(rot) 0.1331 | loss(pos) 0.0296 | loss(seq) 0.2328 | grad 2.2521 | lr 0.0000 | time_forward 1.1930 | time_backward 1.3960 |
[2023-10-24 00:30:42,886::train::INFO] [train] Iter 577141 | loss 1.5213 | loss(rot) 1.4843 | loss(pos) 0.0300 | loss(seq) 0.0070 | grad 4.1344 | lr 0.0000 | time_forward 1.2630 | time_backward 1.4030 |
[2023-10-24 00:30:51,228::train::INFO] [train] Iter 577142 | loss 0.1264 | loss(rot) 0.0245 | loss(pos) 0.0881 | loss(seq) 0.0138 | grad 4.1764 | lr 0.0000 | time_forward 3.4170 | time_backward 4.9110 |
[2023-10-24 00:30:54,082::train::INFO] [train] Iter 577143 | loss 1.7573 | loss(rot) 0.8149 | loss(pos) 0.5432 | loss(seq) 0.3992 | grad 5.3263 | lr 0.0000 | time_forward 1.3130 | time_backward 1.5370 |
[2023-10-24 00:31:01,451::train::INFO] [train] Iter 577144 | loss 0.2709 | loss(rot) 0.0412 | loss(pos) 0.0391 | loss(seq) 0.1906 | grad 2.1558 | lr 0.0000 | time_forward 3.1840 | time_backward 4.1820 |
[2023-10-24 00:31:08,208::train::INFO] [train] Iter 577145 | loss 0.7997 | loss(rot) 0.0390 | loss(pos) 0.7558 | loss(seq) 0.0049 | grad 7.9376 | lr 0.0000 | time_forward 2.9410 | time_backward 3.8130 |
[2023-10-24 00:31:10,829::train::INFO] [train] Iter 577146 | loss 0.2130 | loss(rot) 0.1431 | loss(pos) 0.0461 | loss(seq) 0.0239 | grad 2.5950 | lr 0.0000 | time_forward 1.2320 | time_backward 1.3860 |
[2023-10-24 00:31:20,396::train::INFO] [train] Iter 577147 | loss 1.3944 | loss(rot) 1.3640 | loss(pos) 0.0305 | loss(seq) 0.0000 | grad 2.9016 | lr 0.0000 | time_forward 3.4530 | time_backward 6.1120 |
[2023-10-24 00:31:27,685::train::INFO] [train] Iter 577148 | loss 0.0804 | loss(rot) 0.0621 | loss(pos) 0.0139 | loss(seq) 0.0044 | grad 1.6133 | lr 0.0000 | time_forward 2.8300 | time_backward 4.4550 |
[2023-10-24 00:31:35,471::train::INFO] [train] Iter 577149 | loss 0.7460 | loss(rot) 0.1159 | loss(pos) 0.2441 | loss(seq) 0.3860 | grad 4.9886 | lr 0.0000 | time_forward 3.3620 | time_backward 4.4220 |
[2023-10-24 00:31:42,563::train::INFO] [train] Iter 577150 | loss 0.2041 | loss(rot) 0.1115 | loss(pos) 0.0474 | loss(seq) 0.0452 | grad 2.3775 | lr 0.0000 | time_forward 3.1180 | time_backward 3.9720 |
[2023-10-24 00:31:49,371::train::INFO] [train] Iter 577151 | loss 0.4049 | loss(rot) 0.0469 | loss(pos) 0.2754 | loss(seq) 0.0826 | grad 5.5552 | lr 0.0000 | time_forward 2.9620 | time_backward 3.8430 |
[2023-10-24 00:31:55,701::train::INFO] [train] Iter 577152 | loss 1.4259 | loss(rot) 0.5362 | loss(pos) 0.3709 | loss(seq) 0.5187 | grad 9.9285 | lr 0.0000 | time_forward 2.7420 | time_backward 3.5860 |
[2023-10-24 00:31:58,375::train::INFO] [train] Iter 577153 | loss 0.7949 | loss(rot) 0.3177 | loss(pos) 0.0861 | loss(seq) 0.3911 | grad 4.6558 | lr 0.0000 | time_forward 1.2930 | time_backward 1.3770 |
[2023-10-24 00:32:00,595::train::INFO] [train] Iter 577154 | loss 0.1224 | loss(rot) 0.0944 | loss(pos) 0.0246 | loss(seq) 0.0035 | grad 1.7855 | lr 0.0000 | time_forward 1.0500 | time_backward 1.1670 |
[2023-10-24 00:32:07,808::train::INFO] [train] Iter 577155 | loss 0.3771 | loss(rot) 0.2132 | loss(pos) 0.0331 | loss(seq) 0.1308 | grad 2.9642 | lr 0.0000 | time_forward 3.0050 | time_backward 4.1990 |
[2023-10-24 00:32:17,690::train::INFO] [train] Iter 577156 | loss 2.2988 | loss(rot) 1.7406 | loss(pos) 0.1703 | loss(seq) 0.3879 | grad 5.1406 | lr 0.0000 | time_forward 4.2650 | time_backward 5.6130 |
[2023-10-24 00:32:25,707::train::INFO] [train] Iter 577157 | loss 0.9231 | loss(rot) 0.8428 | loss(pos) 0.0304 | loss(seq) 0.0500 | grad 27.1198 | lr 0.0000 | time_forward 3.5040 | time_backward 4.5110 |
[2023-10-24 00:32:32,795::train::INFO] [train] Iter 577158 | loss 0.0797 | loss(rot) 0.0320 | loss(pos) 0.0178 | loss(seq) 0.0299 | grad 1.3859 | lr 0.0000 | time_forward 3.0880 | time_backward 3.9960 |
[2023-10-24 00:32:40,872::train::INFO] [train] Iter 577159 | loss 1.2488 | loss(rot) 0.8292 | loss(pos) 0.1517 | loss(seq) 0.2679 | grad 4.1801 | lr 0.0000 | time_forward 3.3030 | time_backward 4.7700 |
[2023-10-24 00:32:47,917::train::INFO] [train] Iter 577160 | loss 2.0159 | loss(rot) 1.4674 | loss(pos) 0.1679 | loss(seq) 0.3807 | grad 4.5553 | lr 0.0000 | time_forward 3.0780 | time_backward 3.9640 |
[2023-10-24 00:32:50,332::train::INFO] [train] Iter 577161 | loss 0.3899 | loss(rot) 0.0399 | loss(pos) 0.3131 | loss(seq) 0.0369 | grad 5.2690 | lr 0.0000 | time_forward 1.1460 | time_backward 1.2650 |
[2023-10-24 00:32:53,349::train::INFO] [train] Iter 577162 | loss 0.9998 | loss(rot) 0.6096 | loss(pos) 0.0664 | loss(seq) 0.3239 | grad 3.4163 | lr 0.0000 | time_forward 1.3790 | time_backward 1.6250 |
[2023-10-24 00:33:01,232::train::INFO] [train] Iter 577163 | loss 0.4947 | loss(rot) 0.3407 | loss(pos) 0.0187 | loss(seq) 0.1353 | grad 8.2675 | lr 0.0000 | time_forward 3.2290 | time_backward 4.6450 |
[2023-10-24 00:33:08,576::train::INFO] [train] Iter 577164 | loss 0.7353 | loss(rot) 0.3035 | loss(pos) 0.1278 | loss(seq) 0.3040 | grad 2.7936 | lr 0.0000 | time_forward 3.0560 | time_backward 4.2850 |
[2023-10-24 00:33:11,600::train::INFO] [train] Iter 577165 | loss 0.7979 | loss(rot) 0.2258 | loss(pos) 0.2829 | loss(seq) 0.2893 | grad 3.3156 | lr 0.0000 | time_forward 1.3890 | time_backward 1.6320 |
[2023-10-24 00:33:16,925::train::INFO] [train] Iter 577166 | loss 0.5789 | loss(rot) 0.3527 | loss(pos) 0.0523 | loss(seq) 0.1740 | grad 9.4014 | lr 0.0000 | time_forward 2.2720 | time_backward 3.0430 |
[2023-10-24 00:33:24,229::train::INFO] [train] Iter 577167 | loss 1.5544 | loss(rot) 1.2795 | loss(pos) 0.0735 | loss(seq) 0.2014 | grad 4.9569 | lr 0.0000 | time_forward 3.1640 | time_backward 4.1300 |
[2023-10-24 00:33:26,878::train::INFO] [train] Iter 577168 | loss 0.9640 | loss(rot) 0.6641 | loss(pos) 0.0469 | loss(seq) 0.2529 | grad 3.6897 | lr 0.0000 | time_forward 1.2210 | time_backward 1.4250 |
[2023-10-24 00:33:36,414::train::INFO] [train] Iter 577169 | loss 0.2371 | loss(rot) 0.2166 | loss(pos) 0.0192 | loss(seq) 0.0013 | grad 3.2424 | lr 0.0000 | time_forward 2.4710 | time_backward 7.0620 |
[2023-10-24 00:33:48,969::train::INFO] [train] Iter 577170 | loss 1.1692 | loss(rot) 0.4959 | loss(pos) 0.2658 | loss(seq) 0.4075 | grad 4.0645 | lr 0.0000 | time_forward 6.5180 | time_backward 6.0350 |
[2023-10-24 00:33:51,902::train::INFO] [train] Iter 577171 | loss 1.3170 | loss(rot) 1.2843 | loss(pos) 0.0272 | loss(seq) 0.0056 | grad 7.2972 | lr 0.0000 | time_forward 1.4350 | time_backward 1.4950 |
[2023-10-24 00:34:02,330::train::INFO] [train] Iter 577172 | loss 0.8048 | loss(rot) 0.4914 | loss(pos) 0.0285 | loss(seq) 0.2849 | grad 2.6324 | lr 0.0000 | time_forward 5.5970 | time_backward 4.7930 |
[2023-10-24 00:34:05,112::train::INFO] [train] Iter 577173 | loss 0.4446 | loss(rot) 0.3960 | loss(pos) 0.0466 | loss(seq) 0.0020 | grad 2.8993 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4730 |
[2023-10-24 00:34:14,110::train::INFO] [train] Iter 577174 | loss 0.7063 | loss(rot) 0.0871 | loss(pos) 0.5947 | loss(seq) 0.0245 | grad 5.4061 | lr 0.0000 | time_forward 3.7800 | time_backward 5.2140 |
[2023-10-24 00:34:21,978::train::INFO] [train] Iter 577175 | loss 1.8438 | loss(rot) 1.7376 | loss(pos) 0.0546 | loss(seq) 0.0516 | grad 6.7081 | lr 0.0000 | time_forward 3.3260 | time_backward 4.5400 |
[2023-10-24 00:34:29,119::train::INFO] [train] Iter 577176 | loss 0.5451 | loss(rot) 0.2924 | loss(pos) 0.0818 | loss(seq) 0.1709 | grad 3.5943 | lr 0.0000 | time_forward 3.0920 | time_backward 4.0450 |
[2023-10-24 00:34:36,614::train::INFO] [train] Iter 577177 | loss 0.1615 | loss(rot) 0.1100 | loss(pos) 0.0419 | loss(seq) 0.0096 | grad 1.5986 | lr 0.0000 | time_forward 3.1860 | time_backward 4.3050 |
[2023-10-24 00:34:39,220::train::INFO] [train] Iter 577178 | loss 0.3614 | loss(rot) 0.0489 | loss(pos) 0.2941 | loss(seq) 0.0184 | grad 9.6612 | lr 0.0000 | time_forward 1.2280 | time_backward 1.3740 |
[2023-10-24 00:34:45,321::train::INFO] [train] Iter 577179 | loss 0.2969 | loss(rot) 0.0135 | loss(pos) 0.2824 | loss(seq) 0.0010 | grad 4.7967 | lr 0.0000 | time_forward 2.6510 | time_backward 3.4460 |
[2023-10-24 00:34:53,537::train::INFO] [train] Iter 577180 | loss 0.8351 | loss(rot) 0.2049 | loss(pos) 0.3483 | loss(seq) 0.2819 | grad 4.3324 | lr 0.0000 | time_forward 3.2710 | time_backward 4.9420 |
[2023-10-24 00:34:56,181::train::INFO] [train] Iter 577181 | loss 0.5321 | loss(rot) 0.4280 | loss(pos) 0.0491 | loss(seq) 0.0550 | grad 4.2845 | lr 0.0000 | time_forward 1.2740 | time_backward 1.3680 |
[2023-10-24 00:35:04,528::train::INFO] [train] Iter 577182 | loss 0.4085 | loss(rot) 0.3361 | loss(pos) 0.0345 | loss(seq) 0.0378 | grad 2.4533 | lr 0.0000 | time_forward 3.4280 | time_backward 4.9150 |
[2023-10-24 00:35:07,022::train::INFO] [train] Iter 577183 | loss 0.4363 | loss(rot) 0.0559 | loss(pos) 0.1274 | loss(seq) 0.2530 | grad 4.0905 | lr 0.0000 | time_forward 1.2280 | time_backward 1.2630 |
[2023-10-24 00:35:14,410::train::INFO] [train] Iter 577184 | loss 0.9094 | loss(rot) 0.5156 | loss(pos) 0.2329 | loss(seq) 0.1609 | grad 5.9784 | lr 0.0000 | time_forward 3.2080 | time_backward 4.1770 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.