text
stringlengths 56
1.16k
|
---|
[2023-10-25 16:48:36,054::train::INFO] [train] Iter 598064 | loss 0.3764 | loss(rot) 0.0757 | loss(pos) 0.2731 | loss(seq) 0.0276 | grad 3.5105 | lr 0.0000 | time_forward 2.7560 | time_backward 3.8950
|
[2023-10-25 16:48:43,454::train::INFO] [train] Iter 598065 | loss 0.2149 | loss(rot) 0.1198 | loss(pos) 0.0131 | loss(seq) 0.0821 | grad 1.6290 | lr 0.0000 | time_forward 3.2100 | time_backward 4.1860
|
[2023-10-25 16:48:51,017::train::INFO] [train] Iter 598066 | loss 0.4336 | loss(rot) 0.1852 | loss(pos) 0.0287 | loss(seq) 0.2197 | grad 3.6712 | lr 0.0000 | time_forward 3.3610 | time_backward 4.1980
|
[2023-10-25 16:48:59,045::train::INFO] [train] Iter 598067 | loss 1.1772 | loss(rot) 0.4312 | loss(pos) 0.3394 | loss(seq) 0.4066 | grad 3.2527 | lr 0.0000 | time_forward 3.3850 | time_backward 4.6400
|
[2023-10-25 16:49:04,307::train::INFO] [train] Iter 598068 | loss 0.3377 | loss(rot) 0.1112 | loss(pos) 0.0891 | loss(seq) 0.1375 | grad 2.9483 | lr 0.0000 | time_forward 2.2840 | time_backward 2.9750
|
[2023-10-25 16:49:13,113::train::INFO] [train] Iter 598069 | loss 0.8106 | loss(rot) 0.5998 | loss(pos) 0.0387 | loss(seq) 0.1721 | grad 2.5522 | lr 0.0000 | time_forward 4.2090 | time_backward 4.5940
|
[2023-10-25 16:49:15,496::train::INFO] [train] Iter 598070 | loss 0.5364 | loss(rot) 0.4422 | loss(pos) 0.0657 | loss(seq) 0.0285 | grad 4.7065 | lr 0.0000 | time_forward 1.1350 | time_backward 1.2450
|
[2023-10-25 16:49:23,045::train::INFO] [train] Iter 598071 | loss 0.6398 | loss(rot) 0.6174 | loss(pos) 0.0221 | loss(seq) 0.0003 | grad 7.5353 | lr 0.0000 | time_forward 3.3430 | time_backward 4.2020
|
[2023-10-25 16:49:33,594::train::INFO] [train] Iter 598072 | loss 0.5786 | loss(rot) 0.2429 | loss(pos) 0.0691 | loss(seq) 0.2667 | grad 3.2563 | lr 0.0000 | time_forward 4.5860 | time_backward 5.9600
|
[2023-10-25 16:49:41,235::train::INFO] [train] Iter 598073 | loss 0.2066 | loss(rot) 0.1522 | loss(pos) 0.0269 | loss(seq) 0.0275 | grad 2.0872 | lr 0.0000 | time_forward 3.1420 | time_backward 4.4970
|
[2023-10-25 16:49:50,089::train::INFO] [train] Iter 598074 | loss 0.5208 | loss(rot) 0.1670 | loss(pos) 0.3453 | loss(seq) 0.0085 | grad 5.9308 | lr 0.0000 | time_forward 4.0490 | time_backward 4.8010
|
[2023-10-25 16:49:52,755::train::INFO] [train] Iter 598075 | loss 0.4891 | loss(rot) 0.3432 | loss(pos) 0.0319 | loss(seq) 0.1139 | grad 4.6658 | lr 0.0000 | time_forward 1.2870 | time_backward 1.3770
|
[2023-10-25 16:50:00,231::train::INFO] [train] Iter 598076 | loss 1.0824 | loss(rot) 1.0544 | loss(pos) 0.0199 | loss(seq) 0.0081 | grad 2.0935 | lr 0.0000 | time_forward 3.0230 | time_backward 4.4200
|
[2023-10-25 16:50:09,247::train::INFO] [train] Iter 598077 | loss 0.5706 | loss(rot) 0.2865 | loss(pos) 0.0939 | loss(seq) 0.1902 | grad 4.4155 | lr 0.0000 | time_forward 4.1270 | time_backward 4.8850
|
[2023-10-25 16:50:17,539::train::INFO] [train] Iter 598078 | loss 0.2820 | loss(rot) 0.1258 | loss(pos) 0.1068 | loss(seq) 0.0494 | grad 3.9056 | lr 0.0000 | time_forward 3.4230 | time_backward 4.8670
|
[2023-10-25 16:50:25,797::train::INFO] [train] Iter 598079 | loss 0.4651 | loss(rot) 0.1197 | loss(pos) 0.1073 | loss(seq) 0.2381 | grad 4.0125 | lr 0.0000 | time_forward 3.3650 | time_backward 4.8890
|
[2023-10-25 16:50:33,627::train::INFO] [train] Iter 598080 | loss 0.5861 | loss(rot) 0.5455 | loss(pos) 0.0244 | loss(seq) 0.0163 | grad 5.2546 | lr 0.0000 | time_forward 3.7090 | time_backward 4.1180
|
[2023-10-25 16:50:36,393::train::INFO] [train] Iter 598081 | loss 0.9157 | loss(rot) 0.7762 | loss(pos) 0.0380 | loss(seq) 0.1016 | grad 5.3259 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4370
|
[2023-10-25 16:50:43,388::train::INFO] [train] Iter 598082 | loss 0.4369 | loss(rot) 0.1577 | loss(pos) 0.1035 | loss(seq) 0.1756 | grad 2.5373 | lr 0.0000 | time_forward 2.9990 | time_backward 3.9920
|
[2023-10-25 16:50:50,846::train::INFO] [train] Iter 598083 | loss 1.2611 | loss(rot) 0.7086 | loss(pos) 0.1227 | loss(seq) 0.4298 | grad 3.2616 | lr 0.0000 | time_forward 3.1730 | time_backward 4.2820
|
[2023-10-25 16:50:57,911::train::INFO] [train] Iter 598084 | loss 1.8833 | loss(rot) 1.4827 | loss(pos) 0.2154 | loss(seq) 0.1852 | grad 3.5631 | lr 0.0000 | time_forward 3.1380 | time_backward 3.9230
|
[2023-10-25 16:51:04,115::train::INFO] [train] Iter 598085 | loss 2.0793 | loss(rot) 1.4863 | loss(pos) 0.0535 | loss(seq) 0.5395 | grad 6.2894 | lr 0.0000 | time_forward 2.6830 | time_backward 3.5190
|
[2023-10-25 16:51:06,408::train::INFO] [train] Iter 598086 | loss 0.9262 | loss(rot) 0.3097 | loss(pos) 0.6133 | loss(seq) 0.0032 | grad 11.2825 | lr 0.0000 | time_forward 1.0780 | time_backward 1.2110
|
[2023-10-25 16:51:09,234::train::INFO] [train] Iter 598087 | loss 1.3249 | loss(rot) 1.2065 | loss(pos) 0.0329 | loss(seq) 0.0855 | grad 2.4611 | lr 0.0000 | time_forward 1.2000 | time_backward 1.6210
|
[2023-10-25 16:51:18,301::train::INFO] [train] Iter 598088 | loss 0.2668 | loss(rot) 0.1769 | loss(pos) 0.0278 | loss(seq) 0.0621 | grad 23.6438 | lr 0.0000 | time_forward 4.6900 | time_backward 4.3740
|
[2023-10-25 16:51:27,611::train::INFO] [train] Iter 598089 | loss 1.1869 | loss(rot) 0.8974 | loss(pos) 0.0794 | loss(seq) 0.2101 | grad 3.8913 | lr 0.0000 | time_forward 4.7340 | time_backward 4.5730
|
[2023-10-25 16:51:34,118::train::INFO] [train] Iter 598090 | loss 0.7178 | loss(rot) 0.2702 | loss(pos) 0.1601 | loss(seq) 0.2875 | grad 4.3086 | lr 0.0000 | time_forward 2.8310 | time_backward 3.6730
|
[2023-10-25 16:51:36,831::train::INFO] [train] Iter 598091 | loss 0.9161 | loss(rot) 0.6748 | loss(pos) 0.0620 | loss(seq) 0.1793 | grad 2.1271 | lr 0.0000 | time_forward 1.2380 | time_backward 1.4730
|
[2023-10-25 16:51:45,037::train::INFO] [train] Iter 598092 | loss 0.8562 | loss(rot) 0.3435 | loss(pos) 0.1680 | loss(seq) 0.3447 | grad 3.8699 | lr 0.0000 | time_forward 3.5710 | time_backward 4.6310
|
[2023-10-25 16:51:52,047::train::INFO] [train] Iter 598093 | loss 1.6885 | loss(rot) 1.6466 | loss(pos) 0.0322 | loss(seq) 0.0098 | grad 2.8662 | lr 0.0000 | time_forward 2.7890 | time_backward 4.2170
|
[2023-10-25 16:52:11,299::train::INFO] [train] Iter 598094 | loss 0.2236 | loss(rot) 0.1493 | loss(pos) 0.0142 | loss(seq) 0.0601 | grad 2.4157 | lr 0.0000 | time_forward 14.5490 | time_backward 4.7000
|
[2023-10-25 16:52:14,043::train::INFO] [train] Iter 598095 | loss 0.3956 | loss(rot) 0.1475 | loss(pos) 0.0475 | loss(seq) 0.2006 | grad 2.2346 | lr 0.0000 | time_forward 1.3830 | time_backward 1.3580
|
[2023-10-25 16:52:27,573::train::INFO] [train] Iter 598096 | loss 2.3835 | loss(rot) 1.7203 | loss(pos) 0.1689 | loss(seq) 0.4943 | grad 7.6591 | lr 0.0000 | time_forward 2.8760 | time_backward 10.6500
|
[2023-10-25 16:52:37,563::train::INFO] [train] Iter 598097 | loss 0.3477 | loss(rot) 0.2546 | loss(pos) 0.0171 | loss(seq) 0.0761 | grad 3.3495 | lr 0.0000 | time_forward 6.0890 | time_backward 3.8980
|
[2023-10-25 16:52:40,110::train::INFO] [train] Iter 598098 | loss 0.8133 | loss(rot) 0.7782 | loss(pos) 0.0346 | loss(seq) 0.0005 | grad 6.2871 | lr 0.0000 | time_forward 1.1880 | time_backward 1.3560
|
[2023-10-25 16:52:42,776::train::INFO] [train] Iter 598099 | loss 0.5055 | loss(rot) 0.4453 | loss(pos) 0.0189 | loss(seq) 0.0413 | grad 5.7879 | lr 0.0000 | time_forward 1.2370 | time_backward 1.4120
|
[2023-10-25 16:52:49,862::train::INFO] [train] Iter 598100 | loss 0.1374 | loss(rot) 0.1005 | loss(pos) 0.0300 | loss(seq) 0.0070 | grad 1.6076 | lr 0.0000 | time_forward 3.1340 | time_backward 3.9480
|
[2023-10-25 16:52:52,421::train::INFO] [train] Iter 598101 | loss 0.5932 | loss(rot) 0.3936 | loss(pos) 0.0573 | loss(seq) 0.1424 | grad 3.1719 | lr 0.0000 | time_forward 1.2010 | time_backward 1.3560
|
[2023-10-25 16:52:55,239::train::INFO] [train] Iter 598102 | loss 0.5430 | loss(rot) 0.3984 | loss(pos) 0.0331 | loss(seq) 0.1115 | grad 4.7990 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4710
|
[2023-10-25 16:53:03,228::train::INFO] [train] Iter 598103 | loss 0.2313 | loss(rot) 0.1296 | loss(pos) 0.1017 | loss(seq) 0.0001 | grad 2.7174 | lr 0.0000 | time_forward 3.1570 | time_backward 4.8300
|
[2023-10-25 16:53:10,305::train::INFO] [train] Iter 598104 | loss 0.1175 | loss(rot) 0.0710 | loss(pos) 0.0462 | loss(seq) 0.0003 | grad 2.1177 | lr 0.0000 | time_forward 3.0740 | time_backward 4.0000
|
[2023-10-25 16:53:19,106::train::INFO] [train] Iter 598105 | loss 1.2171 | loss(rot) 1.1486 | loss(pos) 0.0685 | loss(seq) 0.0000 | grad 4.1362 | lr 0.0000 | time_forward 3.1770 | time_backward 5.6200
|
[2023-10-25 16:53:22,142::train::INFO] [train] Iter 598106 | loss 0.7197 | loss(rot) 0.3040 | loss(pos) 0.0355 | loss(seq) 0.3802 | grad 3.4714 | lr 0.0000 | time_forward 1.3280 | time_backward 1.7020
|
[2023-10-25 16:53:30,480::train::INFO] [train] Iter 598107 | loss 0.7407 | loss(rot) 0.3753 | loss(pos) 0.0286 | loss(seq) 0.3368 | grad 3.2813 | lr 0.0000 | time_forward 3.7410 | time_backward 4.5930
|
[2023-10-25 16:53:35,866::train::INFO] [train] Iter 598108 | loss 0.6324 | loss(rot) 0.2137 | loss(pos) 0.0557 | loss(seq) 0.3630 | grad 3.1756 | lr 0.0000 | time_forward 2.3570 | time_backward 3.0250
|
[2023-10-25 16:53:42,632::train::INFO] [train] Iter 598109 | loss 0.1220 | loss(rot) 0.0723 | loss(pos) 0.0498 | loss(seq) 0.0000 | grad 2.3851 | lr 0.0000 | time_forward 2.9110 | time_backward 3.8520
|
[2023-10-25 16:53:48,511::train::INFO] [train] Iter 598110 | loss 0.4733 | loss(rot) 0.2511 | loss(pos) 0.0312 | loss(seq) 0.1909 | grad 3.9853 | lr 0.0000 | time_forward 2.4580 | time_backward 3.4180
|
[2023-10-25 16:53:55,005::train::INFO] [train] Iter 598111 | loss 0.3660 | loss(rot) 0.1496 | loss(pos) 0.0903 | loss(seq) 0.1262 | grad 2.9235 | lr 0.0000 | time_forward 2.7730 | time_backward 3.7180
|
[2023-10-25 16:53:57,601::train::INFO] [train] Iter 598112 | loss 0.4655 | loss(rot) 0.2986 | loss(pos) 0.0630 | loss(seq) 0.1039 | grad 3.8313 | lr 0.0000 | time_forward 1.2350 | time_backward 1.3590
|
[2023-10-25 16:54:06,595::train::INFO] [train] Iter 598113 | loss 0.5129 | loss(rot) 0.2118 | loss(pos) 0.1154 | loss(seq) 0.1858 | grad 3.3950 | lr 0.0000 | time_forward 3.2200 | time_backward 5.7700
|
[2023-10-25 16:54:09,587::train::INFO] [train] Iter 598114 | loss 0.3535 | loss(rot) 0.0874 | loss(pos) 0.2498 | loss(seq) 0.0164 | grad 4.5877 | lr 0.0000 | time_forward 1.5900 | time_backward 1.3990
|
[2023-10-25 16:54:19,444::train::INFO] [train] Iter 598115 | loss 0.9166 | loss(rot) 0.8444 | loss(pos) 0.0345 | loss(seq) 0.0377 | grad 2.8141 | lr 0.0000 | time_forward 5.0270 | time_backward 4.8270
|
[2023-10-25 16:54:21,698::train::INFO] [train] Iter 598116 | loss 0.4258 | loss(rot) 0.2399 | loss(pos) 0.0552 | loss(seq) 0.1307 | grad 2.9138 | lr 0.0000 | time_forward 1.0380 | time_backward 1.2130
|
[2023-10-25 16:54:23,928::train::INFO] [train] Iter 598117 | loss 1.6082 | loss(rot) 1.3529 | loss(pos) 0.1055 | loss(seq) 0.1498 | grad 3.7623 | lr 0.0000 | time_forward 1.0040 | time_backward 1.2160
|
[2023-10-25 16:54:26,620::train::INFO] [train] Iter 598118 | loss 1.1258 | loss(rot) 0.6425 | loss(pos) 0.1177 | loss(seq) 0.3657 | grad 3.4631 | lr 0.0000 | time_forward 1.2330 | time_backward 1.4560
|
[2023-10-25 16:54:29,301::train::INFO] [train] Iter 598119 | loss 1.4973 | loss(rot) 1.4671 | loss(pos) 0.0177 | loss(seq) 0.0125 | grad 5.6151 | lr 0.0000 | time_forward 1.2610 | time_backward 1.4160
|
[2023-10-25 16:54:37,850::train::INFO] [train] Iter 598120 | loss 0.4708 | loss(rot) 0.2228 | loss(pos) 0.0335 | loss(seq) 0.2145 | grad 2.7062 | lr 0.0000 | time_forward 3.1410 | time_backward 5.4040
|
[2023-10-25 16:54:41,483::train::INFO] [train] Iter 598121 | loss 1.7458 | loss(rot) 1.0095 | loss(pos) 0.2738 | loss(seq) 0.4625 | grad 11.3674 | lr 0.0000 | time_forward 1.5510 | time_backward 2.0790
|
[2023-10-25 16:54:49,773::train::INFO] [train] Iter 598122 | loss 0.3923 | loss(rot) 0.1103 | loss(pos) 0.0221 | loss(seq) 0.2599 | grad 3.1615 | lr 0.0000 | time_forward 3.5950 | time_backward 4.6860
|
[2023-10-25 16:54:52,421::train::INFO] [train] Iter 598123 | loss 0.2868 | loss(rot) 0.1279 | loss(pos) 0.0527 | loss(seq) 0.1062 | grad 2.5221 | lr 0.0000 | time_forward 1.2020 | time_backward 1.4370
|
[2023-10-25 16:54:59,248::train::INFO] [train] Iter 598124 | loss 0.1536 | loss(rot) 0.0692 | loss(pos) 0.0238 | loss(seq) 0.0606 | grad 2.5288 | lr 0.0000 | time_forward 3.1380 | time_backward 3.6860
|
[2023-10-25 16:55:13,301::train::INFO] [train] Iter 598125 | loss 1.5927 | loss(rot) 1.1350 | loss(pos) 0.0894 | loss(seq) 0.3683 | grad 11.3657 | lr 0.0000 | time_forward 5.4180 | time_backward 8.6310
|
[2023-10-25 16:56:22,879::train::INFO] [train] Iter 598126 | loss 0.6474 | loss(rot) 0.5934 | loss(pos) 0.0314 | loss(seq) 0.0226 | grad 3.9967 | lr 0.0000 | time_forward 67.5940 | time_backward 1.9800
|
[2023-10-25 16:56:44,664::train::INFO] [train] Iter 598127 | loss 1.3555 | loss(rot) 1.2620 | loss(pos) 0.0682 | loss(seq) 0.0253 | grad 6.9680 | lr 0.0000 | time_forward 15.7170 | time_backward 6.0550
|
[2023-10-25 16:56:54,986::train::INFO] [train] Iter 598128 | loss 1.9467 | loss(rot) 1.8638 | loss(pos) 0.0829 | loss(seq) 0.0000 | grad 3.8684 | lr 0.0000 | time_forward 5.5840 | time_backward 4.7360
|
[2023-10-25 16:57:08,711::train::INFO] [train] Iter 598129 | loss 1.0734 | loss(rot) 0.6775 | loss(pos) 0.0413 | loss(seq) 0.3546 | grad 3.4191 | lr 0.0000 | time_forward 8.0950 | time_backward 5.6270
|
[2023-10-25 16:57:25,015::train::INFO] [train] Iter 598130 | loss 2.3072 | loss(rot) 2.0888 | loss(pos) 0.0937 | loss(seq) 0.1247 | grad 4.1895 | lr 0.0000 | time_forward 10.0260 | time_backward 6.2740
|
[2023-10-25 16:57:36,312::train::INFO] [train] Iter 598131 | loss 1.2549 | loss(rot) 0.9962 | loss(pos) 0.1172 | loss(seq) 0.1415 | grad 4.5001 | lr 0.0000 | time_forward 6.2540 | time_backward 5.0400
|
[2023-10-25 16:57:43,551::train::INFO] [train] Iter 598132 | loss 0.6310 | loss(rot) 0.0849 | loss(pos) 0.3210 | loss(seq) 0.2251 | grad 4.9588 | lr 0.0000 | time_forward 3.0400 | time_backward 4.1960
|
[2023-10-25 16:57:52,829::train::INFO] [train] Iter 598133 | loss 0.4938 | loss(rot) 0.0230 | loss(pos) 0.4529 | loss(seq) 0.0178 | grad 8.4141 | lr 0.0000 | time_forward 4.0540 | time_backward 5.2220
|
[2023-10-25 16:57:55,641::train::INFO] [train] Iter 598134 | loss 0.3792 | loss(rot) 0.1101 | loss(pos) 0.0876 | loss(seq) 0.1814 | grad 3.0448 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4830
|
[2023-10-25 16:57:58,492::train::INFO] [train] Iter 598135 | loss 0.5156 | loss(rot) 0.1533 | loss(pos) 0.0586 | loss(seq) 0.3037 | grad 3.1799 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4800
|
[2023-10-25 16:58:01,391::train::INFO] [train] Iter 598136 | loss 0.2245 | loss(rot) 0.0722 | loss(pos) 0.0806 | loss(seq) 0.0718 | grad 3.3432 | lr 0.0000 | time_forward 1.3700 | time_backward 1.5250
|
[2023-10-25 16:58:07,604::train::INFO] [train] Iter 598137 | loss 0.6640 | loss(rot) 0.4094 | loss(pos) 0.0399 | loss(seq) 0.2147 | grad 5.9449 | lr 0.0000 | time_forward 2.6400 | time_backward 3.5380
|
[2023-10-25 16:58:10,419::train::INFO] [train] Iter 598138 | loss 1.4837 | loss(rot) 1.0238 | loss(pos) 0.0662 | loss(seq) 0.3938 | grad 3.9538 | lr 0.0000 | time_forward 1.3530 | time_backward 1.4600
|
[2023-10-25 16:58:13,967::train::INFO] [train] Iter 598139 | loss 0.7937 | loss(rot) 0.6751 | loss(pos) 0.0410 | loss(seq) 0.0776 | grad 4.8851 | lr 0.0000 | time_forward 1.5600 | time_backward 1.9840
|
[2023-10-25 16:58:16,800::train::INFO] [train] Iter 598140 | loss 0.7784 | loss(rot) 0.7438 | loss(pos) 0.0336 | loss(seq) 0.0010 | grad 3.4205 | lr 0.0000 | time_forward 1.3300 | time_backward 1.4870
|
[2023-10-25 16:58:27,638::train::INFO] [train] Iter 598141 | loss 0.3304 | loss(rot) 0.0725 | loss(pos) 0.0992 | loss(seq) 0.1588 | grad 3.0550 | lr 0.0000 | time_forward 4.7380 | time_backward 6.0980
|
[2023-10-25 16:58:35,638::train::INFO] [train] Iter 598142 | loss 0.6379 | loss(rot) 0.1232 | loss(pos) 0.0259 | loss(seq) 0.4888 | grad 3.3243 | lr 0.0000 | time_forward 3.3540 | time_backward 4.6420
|
[2023-10-25 16:58:45,791::train::INFO] [train] Iter 598143 | loss 1.0332 | loss(rot) 0.8913 | loss(pos) 0.0346 | loss(seq) 0.1073 | grad 17.6304 | lr 0.0000 | time_forward 4.1410 | time_backward 6.0090
|
[2023-10-25 16:58:54,100::train::INFO] [train] Iter 598144 | loss 0.6686 | loss(rot) 0.2788 | loss(pos) 0.0262 | loss(seq) 0.3635 | grad 4.0300 | lr 0.0000 | time_forward 3.5490 | time_backward 4.7580
|
[2023-10-25 16:59:02,992::train::INFO] [train] Iter 598145 | loss 0.5620 | loss(rot) 0.1413 | loss(pos) 0.0675 | loss(seq) 0.3532 | grad 3.4498 | lr 0.0000 | time_forward 3.8170 | time_backward 5.0710
|
[2023-10-25 16:59:11,740::train::INFO] [train] Iter 598146 | loss 1.0515 | loss(rot) 0.0046 | loss(pos) 1.0467 | loss(seq) 0.0001 | grad 12.1778 | lr 0.0000 | time_forward 3.7600 | time_backward 4.9840
|
[2023-10-25 16:59:22,245::train::INFO] [train] Iter 598147 | loss 0.5444 | loss(rot) 0.0788 | loss(pos) 0.0260 | loss(seq) 0.4396 | grad 2.2414 | lr 0.0000 | time_forward 5.6410 | time_backward 4.8610
|
[2023-10-25 16:59:24,930::train::INFO] [train] Iter 598148 | loss 0.9193 | loss(rot) 0.6153 | loss(pos) 0.0173 | loss(seq) 0.2867 | grad 4.2392 | lr 0.0000 | time_forward 1.3010 | time_backward 1.3810
|
[2023-10-25 16:59:32,786::train::INFO] [train] Iter 598149 | loss 0.8655 | loss(rot) 0.6132 | loss(pos) 0.0228 | loss(seq) 0.2295 | grad 1.8101 | lr 0.0000 | time_forward 3.6170 | time_backward 4.2160
|
[2023-10-25 16:59:35,231::train::INFO] [train] Iter 598150 | loss 0.6622 | loss(rot) 0.1328 | loss(pos) 0.1579 | loss(seq) 0.3715 | grad 3.5219 | lr 0.0000 | time_forward 1.1880 | time_backward 1.2540
|
[2023-10-25 16:59:37,989::train::INFO] [train] Iter 598151 | loss 0.5864 | loss(rot) 0.5352 | loss(pos) 0.0238 | loss(seq) 0.0274 | grad 2.3903 | lr 0.0000 | time_forward 1.2830 | time_backward 1.4580
|
[2023-10-25 16:59:44,478::train::INFO] [train] Iter 598152 | loss 1.0881 | loss(rot) 0.7673 | loss(pos) 0.0753 | loss(seq) 0.2456 | grad 4.5490 | lr 0.0000 | time_forward 2.8330 | time_backward 3.6530
|
[2023-10-25 16:59:50,278::train::INFO] [train] Iter 598153 | loss 0.3033 | loss(rot) 0.1580 | loss(pos) 0.0714 | loss(seq) 0.0738 | grad 2.5538 | lr 0.0000 | time_forward 2.4950 | time_backward 3.3020
|
[2023-10-25 16:59:56,799::train::INFO] [train] Iter 598154 | loss 1.6225 | loss(rot) 0.7270 | loss(pos) 0.4059 | loss(seq) 0.4896 | grad 4.9891 | lr 0.0000 | time_forward 2.7850 | time_backward 3.7330
|
[2023-10-25 17:00:00,757::train::INFO] [train] Iter 598155 | loss 0.1096 | loss(rot) 0.0957 | loss(pos) 0.0134 | loss(seq) 0.0004 | grad 1.6173 | lr 0.0000 | time_forward 1.7790 | time_backward 2.1760
|
[2023-10-25 17:00:07,691::train::INFO] [train] Iter 598156 | loss 2.6116 | loss(rot) 1.8998 | loss(pos) 0.4263 | loss(seq) 0.2855 | grad 6.8613 | lr 0.0000 | time_forward 2.9350 | time_backward 3.9710
|
[2023-10-25 17:00:15,393::train::INFO] [train] Iter 598157 | loss 0.6382 | loss(rot) 0.1578 | loss(pos) 0.1508 | loss(seq) 0.3297 | grad 3.4792 | lr 0.0000 | time_forward 3.3030 | time_backward 4.3960
|
[2023-10-25 17:00:22,677::train::INFO] [train] Iter 598158 | loss 0.5582 | loss(rot) 0.0786 | loss(pos) 0.1091 | loss(seq) 0.3705 | grad 5.5780 | lr 0.0000 | time_forward 3.1370 | time_backward 4.1430
|
[2023-10-25 17:00:25,494::train::INFO] [train] Iter 598159 | loss 0.3422 | loss(rot) 0.3183 | loss(pos) 0.0195 | loss(seq) 0.0043 | grad 3.7627 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4770
|
[2023-10-25 17:00:32,583::train::INFO] [train] Iter 598160 | loss 0.1923 | loss(rot) 0.1130 | loss(pos) 0.0198 | loss(seq) 0.0594 | grad 1.9367 | lr 0.0000 | time_forward 2.9940 | time_backward 4.0910
|
[2023-10-25 17:00:38,823::train::INFO] [train] Iter 598161 | loss 2.1469 | loss(rot) 1.7907 | loss(pos) 0.0452 | loss(seq) 0.3110 | grad 4.2201 | lr 0.0000 | time_forward 2.7320 | time_backward 3.5050
|
[2023-10-25 17:00:45,892::train::INFO] [train] Iter 598162 | loss 0.5054 | loss(rot) 0.0995 | loss(pos) 0.3917 | loss(seq) 0.0143 | grad 6.8757 | lr 0.0000 | time_forward 3.0220 | time_backward 4.0450
|
[2023-10-25 17:00:54,487::train::INFO] [train] Iter 598163 | loss 0.2986 | loss(rot) 0.1041 | loss(pos) 0.0424 | loss(seq) 0.1520 | grad 1.9919 | lr 0.0000 | time_forward 3.5940 | time_backward 4.9970
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.