text
stringlengths 56
1.16k
|
|---|
[2023-09-02 12:04:42,906::train::INFO] [train] Iter 09686 | loss 0.8726 | loss(rot) 0.1165 | loss(pos) 0.6790 | loss(seq) 0.0771 | grad 4.9365 | lr 0.0010 | time_forward 1.3920 | time_backward 1.4580 |
[2023-09-02 12:04:51,867::train::INFO] [train] Iter 09687 | loss 1.2961 | loss(rot) 0.5858 | loss(pos) 0.2467 | loss(seq) 0.4637 | grad 2.9685 | lr 0.0010 | time_forward 3.9100 | time_backward 5.0330 |
[2023-09-02 12:05:01,817::train::INFO] [train] Iter 09688 | loss 2.6529 | loss(rot) 2.0833 | loss(pos) 0.2167 | loss(seq) 0.3530 | grad 7.7810 | lr 0.0010 | time_forward 4.0360 | time_backward 5.9100 |
[2023-09-02 12:05:11,244::train::INFO] [train] Iter 09689 | loss 2.1282 | loss(rot) 0.0340 | loss(pos) 2.0873 | loss(seq) 0.0069 | grad 7.3935 | lr 0.0010 | time_forward 3.9660 | time_backward 5.2280 |
[2023-09-02 12:05:13,260::train::INFO] [train] Iter 09690 | loss 2.5931 | loss(rot) 1.9167 | loss(pos) 0.2735 | loss(seq) 0.4030 | grad 4.8501 | lr 0.0010 | time_forward 0.9120 | time_backward 1.1000 |
[2023-09-02 12:05:21,885::train::INFO] [train] Iter 09691 | loss 2.0286 | loss(rot) 1.1486 | loss(pos) 0.3127 | loss(seq) 0.5674 | grad 5.3258 | lr 0.0010 | time_forward 3.6890 | time_backward 4.9340 |
[2023-09-02 12:05:24,649::train::INFO] [train] Iter 09692 | loss 1.0382 | loss(rot) 0.8820 | loss(pos) 0.1419 | loss(seq) 0.0142 | grad 4.2973 | lr 0.0010 | time_forward 1.2700 | time_backward 1.4900 |
[2023-09-02 12:05:32,669::train::INFO] [train] Iter 09693 | loss 1.7798 | loss(rot) 1.1104 | loss(pos) 0.3297 | loss(seq) 0.3397 | grad 6.1788 | lr 0.0010 | time_forward 3.3840 | time_backward 4.6190 |
[2023-09-02 12:05:42,685::train::INFO] [train] Iter 09694 | loss 1.6773 | loss(rot) 0.7993 | loss(pos) 0.4195 | loss(seq) 0.4585 | grad 4.5221 | lr 0.0010 | time_forward 4.0290 | time_backward 5.9850 |
[2023-09-02 12:05:51,095::train::INFO] [train] Iter 09695 | loss 0.9571 | loss(rot) 0.3629 | loss(pos) 0.2664 | loss(seq) 0.3279 | grad 3.1216 | lr 0.0010 | time_forward 3.4890 | time_backward 4.9170 |
[2023-09-02 12:05:53,778::train::INFO] [train] Iter 09696 | loss 1.3727 | loss(rot) 0.4960 | loss(pos) 0.4543 | loss(seq) 0.4223 | grad 4.2679 | lr 0.0010 | time_forward 1.2530 | time_backward 1.4250 |
[2023-09-02 12:06:02,496::train::INFO] [train] Iter 09697 | loss 2.1161 | loss(rot) 1.8766 | loss(pos) 0.1026 | loss(seq) 0.1369 | grad 5.7537 | lr 0.0010 | time_forward 3.7190 | time_backward 4.9970 |
[2023-09-02 12:06:04,785::train::INFO] [train] Iter 09698 | loss 1.1671 | loss(rot) 0.3027 | loss(pos) 0.7987 | loss(seq) 0.0658 | grad 4.0364 | lr 0.0010 | time_forward 1.0670 | time_backward 1.2190 |
[2023-09-02 12:06:13,213::train::INFO] [train] Iter 09699 | loss 0.4241 | loss(rot) 0.1921 | loss(pos) 0.1950 | loss(seq) 0.0370 | grad 2.7487 | lr 0.0010 | time_forward 3.5930 | time_backward 4.8310 |
[2023-09-02 12:06:22,473::train::INFO] [train] Iter 09700 | loss 1.9874 | loss(rot) 1.8900 | loss(pos) 0.0925 | loss(seq) 0.0049 | grad 4.9476 | lr 0.0010 | time_forward 3.9260 | time_backward 5.3310 |
[2023-09-02 12:06:25,200::train::INFO] [train] Iter 09701 | loss 4.5020 | loss(rot) 0.2947 | loss(pos) 4.2073 | loss(seq) 0.0000 | grad 7.7989 | lr 0.0010 | time_forward 1.2620 | time_backward 1.4630 |
[2023-09-02 12:06:27,930::train::INFO] [train] Iter 09702 | loss 1.6022 | loss(rot) 0.8062 | loss(pos) 0.4008 | loss(seq) 0.3952 | grad 3.9447 | lr 0.0010 | time_forward 1.2950 | time_backward 1.4310 |
[2023-09-02 12:06:36,615::train::INFO] [train] Iter 09703 | loss 1.7959 | loss(rot) 1.1849 | loss(pos) 0.1859 | loss(seq) 0.4252 | grad 5.2657 | lr 0.0010 | time_forward 3.6600 | time_backward 5.0220 |
[2023-09-02 12:06:44,938::train::INFO] [train] Iter 09704 | loss 0.9405 | loss(rot) 0.4097 | loss(pos) 0.3292 | loss(seq) 0.2016 | grad 4.5281 | lr 0.0010 | time_forward 3.5110 | time_backward 4.8080 |
[2023-09-02 12:06:53,084::train::INFO] [train] Iter 09705 | loss 0.9015 | loss(rot) 0.3856 | loss(pos) 0.1963 | loss(seq) 0.3196 | grad 3.7660 | lr 0.0010 | time_forward 3.3390 | time_backward 4.8030 |
[2023-09-02 12:06:55,829::train::INFO] [train] Iter 09706 | loss 0.7878 | loss(rot) 0.1346 | loss(pos) 0.6149 | loss(seq) 0.0384 | grad 4.8041 | lr 0.0010 | time_forward 1.2850 | time_backward 1.4580 |
[2023-09-02 12:06:58,563::train::INFO] [train] Iter 09707 | loss 1.6012 | loss(rot) 0.6039 | loss(pos) 0.4134 | loss(seq) 0.5838 | grad 4.5646 | lr 0.0010 | time_forward 1.2900 | time_backward 1.4400 |
[2023-09-02 12:07:01,322::train::INFO] [train] Iter 09708 | loss 1.0895 | loss(rot) 0.2310 | loss(pos) 0.8306 | loss(seq) 0.0279 | grad 4.3780 | lr 0.0010 | time_forward 1.2910 | time_backward 1.4650 |
[2023-09-02 12:07:11,265::train::INFO] [train] Iter 09709 | loss 1.9575 | loss(rot) 1.3991 | loss(pos) 0.2091 | loss(seq) 0.3492 | grad 6.3076 | lr 0.0010 | time_forward 3.9900 | time_backward 5.9500 |
[2023-09-02 12:07:20,005::train::INFO] [train] Iter 09710 | loss 1.2486 | loss(rot) 0.6514 | loss(pos) 0.2154 | loss(seq) 0.3818 | grad 5.3131 | lr 0.0010 | time_forward 3.6830 | time_backward 5.0530 |
[2023-09-02 12:07:29,933::train::INFO] [train] Iter 09711 | loss 2.0762 | loss(rot) 1.6590 | loss(pos) 0.1385 | loss(seq) 0.2787 | grad 4.5722 | lr 0.0010 | time_forward 4.0770 | time_backward 5.8470 |
[2023-09-02 12:07:32,704::train::INFO] [train] Iter 09712 | loss 0.7862 | loss(rot) 0.0774 | loss(pos) 0.6537 | loss(seq) 0.0551 | grad 7.3260 | lr 0.0010 | time_forward 1.3030 | time_backward 1.4640 |
[2023-09-02 12:07:35,053::train::INFO] [train] Iter 09713 | loss 1.5655 | loss(rot) 0.8963 | loss(pos) 0.2900 | loss(seq) 0.3792 | grad 4.7813 | lr 0.0010 | time_forward 1.0950 | time_backward 1.2510 |
[2023-09-02 12:07:37,757::train::INFO] [train] Iter 09714 | loss 0.8573 | loss(rot) 0.3128 | loss(pos) 0.2095 | loss(seq) 0.3350 | grad 2.7939 | lr 0.0010 | time_forward 1.2530 | time_backward 1.4460 |
[2023-09-02 12:07:44,429::train::INFO] [train] Iter 09715 | loss 1.5750 | loss(rot) 0.5256 | loss(pos) 0.4988 | loss(seq) 0.5507 | grad 4.4421 | lr 0.0010 | time_forward 2.8380 | time_backward 3.8290 |
[2023-09-02 12:07:54,470::train::INFO] [train] Iter 09716 | loss 2.3967 | loss(rot) 1.7889 | loss(pos) 0.2313 | loss(seq) 0.3766 | grad 5.6572 | lr 0.0010 | time_forward 4.1140 | time_backward 5.9230 |
[2023-09-02 12:08:04,126::train::INFO] [train] Iter 09717 | loss 1.3598 | loss(rot) 0.6302 | loss(pos) 0.5916 | loss(seq) 0.1380 | grad 5.7438 | lr 0.0010 | time_forward 4.0190 | time_backward 5.6330 |
[2023-09-02 12:08:12,707::train::INFO] [train] Iter 09718 | loss 1.1212 | loss(rot) 0.5907 | loss(pos) 0.2178 | loss(seq) 0.3126 | grad 4.1096 | lr 0.0010 | time_forward 3.6790 | time_backward 4.8980 |
[2023-09-02 12:08:22,586::train::INFO] [train] Iter 09719 | loss 3.0551 | loss(rot) 1.6257 | loss(pos) 0.9304 | loss(seq) 0.4990 | grad 6.1330 | lr 0.0010 | time_forward 4.2070 | time_backward 5.6680 |
[2023-09-02 12:08:32,084::train::INFO] [train] Iter 09720 | loss 1.2975 | loss(rot) 0.5934 | loss(pos) 0.6663 | loss(seq) 0.0378 | grad 3.5755 | lr 0.0010 | time_forward 3.9830 | time_backward 5.5120 |
[2023-09-02 12:08:41,861::train::INFO] [train] Iter 09721 | loss 1.9261 | loss(rot) 0.5554 | loss(pos) 1.1035 | loss(seq) 0.2673 | grad 5.8060 | lr 0.0010 | time_forward 4.0030 | time_backward 5.7700 |
[2023-09-02 12:08:51,655::train::INFO] [train] Iter 09722 | loss 1.9552 | loss(rot) 1.6916 | loss(pos) 0.1309 | loss(seq) 0.1328 | grad 3.7591 | lr 0.0010 | time_forward 3.9680 | time_backward 5.8230 |
[2023-09-02 12:09:01,250::train::INFO] [train] Iter 09723 | loss 1.1252 | loss(rot) 0.4228 | loss(pos) 0.4084 | loss(seq) 0.2940 | grad 4.1895 | lr 0.0010 | time_forward 3.9310 | time_backward 5.6620 |
[2023-09-02 12:09:10,341::train::INFO] [train] Iter 09724 | loss 1.7661 | loss(rot) 1.1510 | loss(pos) 0.1746 | loss(seq) 0.4405 | grad 7.6480 | lr 0.0010 | time_forward 3.7320 | time_backward 5.3560 |
[2023-09-02 12:09:19,401::train::INFO] [train] Iter 09725 | loss 1.4934 | loss(rot) 1.1329 | loss(pos) 0.1334 | loss(seq) 0.2271 | grad 4.5877 | lr 0.0010 | time_forward 3.7740 | time_backward 5.2830 |
[2023-09-02 12:09:29,412::train::INFO] [train] Iter 09726 | loss 2.1627 | loss(rot) 0.2295 | loss(pos) 1.9276 | loss(seq) 0.0057 | grad 4.3312 | lr 0.0010 | time_forward 4.2470 | time_backward 5.7600 |
[2023-09-02 12:09:39,345::train::INFO] [train] Iter 09727 | loss 1.9773 | loss(rot) 0.1684 | loss(pos) 1.8045 | loss(seq) 0.0044 | grad 6.0248 | lr 0.0010 | time_forward 3.9600 | time_backward 5.9690 |
[2023-09-02 12:09:47,849::train::INFO] [train] Iter 09728 | loss 1.1400 | loss(rot) 0.4879 | loss(pos) 0.1425 | loss(seq) 0.5097 | grad 7.1184 | lr 0.0010 | time_forward 3.5770 | time_backward 4.9250 |
[2023-09-02 12:09:56,675::train::INFO] [train] Iter 09729 | loss 1.8716 | loss(rot) 1.6497 | loss(pos) 0.1162 | loss(seq) 0.1057 | grad 5.0299 | lr 0.0010 | time_forward 3.6770 | time_backward 5.1450 |
[2023-09-02 12:10:05,448::train::INFO] [train] Iter 09730 | loss 2.0804 | loss(rot) 0.9200 | loss(pos) 0.7355 | loss(seq) 0.4249 | grad 6.1194 | lr 0.0010 | time_forward 3.7190 | time_backward 5.0510 |
[2023-09-02 12:10:13,987::train::INFO] [train] Iter 09731 | loss 1.9767 | loss(rot) 1.4751 | loss(pos) 0.3904 | loss(seq) 0.1111 | grad 5.7668 | lr 0.0010 | time_forward 3.5520 | time_backward 4.9830 |
[2023-09-02 12:10:22,569::train::INFO] [train] Iter 09732 | loss 5.4996 | loss(rot) 0.0232 | loss(pos) 5.4764 | loss(seq) 0.0000 | grad 8.4910 | lr 0.0010 | time_forward 3.5670 | time_backward 5.0120 |
[2023-09-02 12:10:24,823::train::INFO] [train] Iter 09733 | loss 2.4097 | loss(rot) 1.5956 | loss(pos) 0.2610 | loss(seq) 0.5531 | grad 5.9385 | lr 0.0010 | time_forward 1.0350 | time_backward 1.2160 |
[2023-09-02 12:10:33,422::train::INFO] [train] Iter 09734 | loss 1.9142 | loss(rot) 1.1004 | loss(pos) 0.4233 | loss(seq) 0.3906 | grad 6.0636 | lr 0.0010 | time_forward 3.6690 | time_backward 4.9280 |
[2023-09-02 12:10:36,056::train::INFO] [train] Iter 09735 | loss 1.0378 | loss(rot) 0.5164 | loss(pos) 0.2288 | loss(seq) 0.2926 | grad 3.0646 | lr 0.0010 | time_forward 1.2090 | time_backward 1.4220 |
[2023-09-02 12:10:45,229::train::INFO] [train] Iter 09736 | loss 1.6137 | loss(rot) 0.6249 | loss(pos) 0.5459 | loss(seq) 0.4429 | grad 3.7183 | lr 0.0010 | time_forward 3.8210 | time_backward 5.3490 |
[2023-09-02 12:10:53,524::train::INFO] [train] Iter 09737 | loss 3.1501 | loss(rot) 2.4937 | loss(pos) 0.2180 | loss(seq) 0.4384 | grad 3.6762 | lr 0.0010 | time_forward 3.4780 | time_backward 4.8130 |
[2023-09-02 12:11:02,518::train::INFO] [train] Iter 09738 | loss 1.5203 | loss(rot) 0.2523 | loss(pos) 1.2283 | loss(seq) 0.0397 | grad 5.2733 | lr 0.0010 | time_forward 3.7330 | time_backward 5.2570 |
[2023-09-02 12:11:11,086::train::INFO] [train] Iter 09739 | loss 1.2508 | loss(rot) 1.0077 | loss(pos) 0.1227 | loss(seq) 0.1203 | grad 4.4330 | lr 0.0010 | time_forward 3.6450 | time_backward 4.9200 |
[2023-09-02 12:11:18,960::train::INFO] [train] Iter 09740 | loss 1.5463 | loss(rot) 0.4534 | loss(pos) 0.7554 | loss(seq) 0.3375 | grad 6.4441 | lr 0.0010 | time_forward 3.2970 | time_backward 4.5740 |
[2023-09-02 12:11:21,163::train::INFO] [train] Iter 09741 | loss 1.5950 | loss(rot) 1.2781 | loss(pos) 0.2209 | loss(seq) 0.0960 | grad 4.0697 | lr 0.0010 | time_forward 1.0180 | time_backward 1.1810 |
[2023-09-02 12:11:27,550::train::INFO] [train] Iter 09742 | loss 2.0261 | loss(rot) 1.2084 | loss(pos) 0.2990 | loss(seq) 0.5187 | grad 6.5795 | lr 0.0010 | time_forward 2.6730 | time_backward 3.7100 |
[2023-09-02 12:11:30,172::train::INFO] [train] Iter 09743 | loss 1.4564 | loss(rot) 0.6882 | loss(pos) 0.2904 | loss(seq) 0.4778 | grad 5.7180 | lr 0.0010 | time_forward 1.2290 | time_backward 1.3910 |
[2023-09-02 12:11:38,094::train::INFO] [train] Iter 09744 | loss 1.6086 | loss(rot) 1.2198 | loss(pos) 0.1204 | loss(seq) 0.2684 | grad 4.6321 | lr 0.0010 | time_forward 3.3130 | time_backward 4.5700 |
[2023-09-02 12:11:47,069::train::INFO] [train] Iter 09745 | loss 2.3422 | loss(rot) 1.9467 | loss(pos) 0.2184 | loss(seq) 0.1771 | grad 3.8570 | lr 0.0010 | time_forward 3.8080 | time_backward 5.1640 |
[2023-09-02 12:11:55,034::train::INFO] [train] Iter 09746 | loss 1.4537 | loss(rot) 1.0380 | loss(pos) 0.1519 | loss(seq) 0.2637 | grad 5.6463 | lr 0.0010 | time_forward 3.3420 | time_backward 4.6180 |
[2023-09-02 12:12:05,001::train::INFO] [train] Iter 09747 | loss 1.8858 | loss(rot) 1.4392 | loss(pos) 0.2746 | loss(seq) 0.1720 | grad 5.0973 | lr 0.0010 | time_forward 4.0070 | time_backward 5.9570 |
[2023-09-02 12:12:14,985::train::INFO] [train] Iter 09748 | loss 0.9005 | loss(rot) 0.0723 | loss(pos) 0.8125 | loss(seq) 0.0157 | grad 2.2092 | lr 0.0010 | time_forward 3.9760 | time_backward 6.0040 |
[2023-09-02 12:12:22,342::train::INFO] [train] Iter 09749 | loss 2.2014 | loss(rot) 1.8732 | loss(pos) 0.2825 | loss(seq) 0.0457 | grad 5.6316 | lr 0.0010 | time_forward 3.0850 | time_backward 4.2680 |
[2023-09-02 12:12:32,328::train::INFO] [train] Iter 09750 | loss 1.4213 | loss(rot) 0.6150 | loss(pos) 0.1995 | loss(seq) 0.6068 | grad 4.3292 | lr 0.0010 | time_forward 4.0070 | time_backward 5.9760 |
[2023-09-02 12:12:40,684::train::INFO] [train] Iter 09751 | loss 1.7873 | loss(rot) 1.6604 | loss(pos) 0.1218 | loss(seq) 0.0051 | grad 6.3456 | lr 0.0010 | time_forward 3.5060 | time_backward 4.8480 |
[2023-09-02 12:12:43,316::train::INFO] [train] Iter 09752 | loss 2.1433 | loss(rot) 1.7398 | loss(pos) 0.1094 | loss(seq) 0.2941 | grad 4.2841 | lr 0.0010 | time_forward 1.2350 | time_backward 1.3940 |
[2023-09-02 12:12:45,560::train::INFO] [train] Iter 09753 | loss 0.9684 | loss(rot) 0.0244 | loss(pos) 0.9401 | loss(seq) 0.0039 | grad 2.8974 | lr 0.0010 | time_forward 1.0610 | time_backward 1.1800 |
[2023-09-02 12:12:53,888::train::INFO] [train] Iter 09754 | loss 1.8098 | loss(rot) 1.5071 | loss(pos) 0.0880 | loss(seq) 0.2147 | grad 3.7284 | lr 0.0010 | time_forward 3.4870 | time_backward 4.8370 |
[2023-09-02 12:13:02,500::train::INFO] [train] Iter 09755 | loss 1.4448 | loss(rot) 0.6705 | loss(pos) 0.2851 | loss(seq) 0.4892 | grad 3.6866 | lr 0.0010 | time_forward 3.6250 | time_backward 4.9850 |
[2023-09-02 12:13:10,534::train::INFO] [train] Iter 09756 | loss 2.1163 | loss(rot) 1.9452 | loss(pos) 0.0978 | loss(seq) 0.0733 | grad 4.2805 | lr 0.0010 | time_forward 3.3750 | time_backward 4.6550 |
[2023-09-02 12:13:17,606::train::INFO] [train] Iter 09757 | loss 1.1217 | loss(rot) 0.1409 | loss(pos) 0.7084 | loss(seq) 0.2724 | grad 3.7555 | lr 0.0010 | time_forward 2.9970 | time_backward 4.0710 |
[2023-09-02 12:13:26,282::train::INFO] [train] Iter 09758 | loss 1.6866 | loss(rot) 1.5705 | loss(pos) 0.0837 | loss(seq) 0.0324 | grad 3.7759 | lr 0.0010 | time_forward 3.6920 | time_backward 4.9810 |
[2023-09-02 12:13:36,306::train::INFO] [train] Iter 09759 | loss 1.9072 | loss(rot) 0.1014 | loss(pos) 1.8055 | loss(seq) 0.0004 | grad 5.3204 | lr 0.0010 | time_forward 4.0880 | time_backward 5.9320 |
[2023-09-02 12:13:45,445::train::INFO] [train] Iter 09760 | loss 2.1567 | loss(rot) 1.5293 | loss(pos) 0.2087 | loss(seq) 0.4186 | grad 4.3991 | lr 0.0010 | time_forward 3.8460 | time_backward 5.2900 |
[2023-09-02 12:13:55,435::train::INFO] [train] Iter 09761 | loss 2.1899 | loss(rot) 2.0045 | loss(pos) 0.1853 | loss(seq) 0.0000 | grad 5.0158 | lr 0.0010 | time_forward 3.9880 | time_backward 5.9980 |
[2023-09-02 12:14:03,874::train::INFO] [train] Iter 09762 | loss 1.7520 | loss(rot) 0.6044 | loss(pos) 0.6785 | loss(seq) 0.4691 | grad 3.0515 | lr 0.0010 | time_forward 3.5660 | time_backward 4.8690 |
[2023-09-02 12:14:13,100::train::INFO] [train] Iter 09763 | loss 2.4277 | loss(rot) 1.4475 | loss(pos) 0.4124 | loss(seq) 0.5678 | grad 3.8333 | lr 0.0010 | time_forward 3.8890 | time_backward 5.3340 |
[2023-09-02 12:14:21,166::train::INFO] [train] Iter 09764 | loss 1.4009 | loss(rot) 0.6748 | loss(pos) 0.4228 | loss(seq) 0.3034 | grad 2.6256 | lr 0.0010 | time_forward 3.3820 | time_backward 4.6790 |
[2023-09-02 12:14:33,139::train::INFO] [train] Iter 09765 | loss 2.3789 | loss(rot) 2.1028 | loss(pos) 0.2761 | loss(seq) 0.0000 | grad 6.6714 | lr 0.0010 | time_forward 4.0550 | time_backward 7.9140 |
[2023-09-02 12:14:43,007::train::INFO] [train] Iter 09766 | loss 1.7143 | loss(rot) 1.2123 | loss(pos) 0.1395 | loss(seq) 0.3625 | grad 4.8627 | lr 0.0010 | time_forward 4.1300 | time_backward 5.7350 |
[2023-09-02 12:14:50,087::train::INFO] [train] Iter 09767 | loss 0.7745 | loss(rot) 0.1949 | loss(pos) 0.5415 | loss(seq) 0.0381 | grad 4.7702 | lr 0.0010 | time_forward 2.9880 | time_backward 4.0890 |
[2023-09-02 12:15:00,105::train::INFO] [train] Iter 09768 | loss 1.9486 | loss(rot) 1.0534 | loss(pos) 0.3719 | loss(seq) 0.5233 | grad 4.2454 | lr 0.0010 | time_forward 4.2880 | time_backward 5.7190 |
[2023-09-02 12:15:10,070::train::INFO] [train] Iter 09769 | loss 0.9706 | loss(rot) 0.1058 | loss(pos) 0.7153 | loss(seq) 0.1495 | grad 4.5255 | lr 0.0010 | time_forward 4.1440 | time_backward 5.8170 |
[2023-09-02 12:15:18,184::train::INFO] [train] Iter 09770 | loss 1.8211 | loss(rot) 0.0258 | loss(pos) 1.7932 | loss(seq) 0.0022 | grad 8.4917 | lr 0.0010 | time_forward 3.5020 | time_backward 4.6090 |
[2023-09-02 12:15:20,907::train::INFO] [train] Iter 09771 | loss 1.9989 | loss(rot) 1.6860 | loss(pos) 0.1611 | loss(seq) 0.1518 | grad 4.9053 | lr 0.0010 | time_forward 1.2950 | time_backward 1.4250 |
[2023-09-02 12:15:23,649::train::INFO] [train] Iter 09772 | loss 1.2461 | loss(rot) 0.5340 | loss(pos) 0.1831 | loss(seq) 0.5290 | grad 3.8697 | lr 0.0010 | time_forward 1.2990 | time_backward 1.4390 |
[2023-09-02 12:15:26,149::train::INFO] [train] Iter 09773 | loss 2.1179 | loss(rot) 1.2494 | loss(pos) 0.4465 | loss(seq) 0.4219 | grad 6.5424 | lr 0.0010 | time_forward 1.1700 | time_backward 1.2880 |
[2023-09-02 12:15:35,825::train::INFO] [train] Iter 09774 | loss 1.6325 | loss(rot) 0.5938 | loss(pos) 0.5189 | loss(seq) 0.5199 | grad 3.8165 | lr 0.0010 | time_forward 3.9570 | time_backward 5.7160 |
[2023-09-02 12:15:45,524::train::INFO] [train] Iter 09775 | loss 2.3809 | loss(rot) 1.6923 | loss(pos) 0.2422 | loss(seq) 0.4464 | grad 5.6150 | lr 0.0010 | time_forward 3.9300 | time_backward 5.7540 |
[2023-09-02 12:15:53,957::train::INFO] [train] Iter 09776 | loss 1.2628 | loss(rot) 0.7085 | loss(pos) 0.1127 | loss(seq) 0.4417 | grad 3.9668 | lr 0.0010 | time_forward 3.4920 | time_backward 4.9370 |
[2023-09-02 12:16:02,361::train::INFO] [train] Iter 09777 | loss 2.7077 | loss(rot) 2.4955 | loss(pos) 0.2121 | loss(seq) 0.0001 | grad 4.8457 | lr 0.0010 | time_forward 3.4700 | time_backward 4.9300 |
[2023-09-02 12:16:05,014::train::INFO] [train] Iter 09778 | loss 1.3834 | loss(rot) 0.7533 | loss(pos) 0.1482 | loss(seq) 0.4819 | grad 5.3267 | lr 0.0010 | time_forward 1.2380 | time_backward 1.4130 |
[2023-09-02 12:16:11,506::train::INFO] [train] Iter 09779 | loss 0.9924 | loss(rot) 0.2287 | loss(pos) 0.4846 | loss(seq) 0.2791 | grad 4.2450 | lr 0.0010 | time_forward 2.7290 | time_backward 3.7600 |
[2023-09-02 12:16:20,604::train::INFO] [train] Iter 09780 | loss 2.2590 | loss(rot) 1.7346 | loss(pos) 0.1069 | loss(seq) 0.4175 | grad 6.1368 | lr 0.0010 | time_forward 3.7980 | time_backward 5.2960 |
[2023-09-02 12:16:23,301::train::INFO] [train] Iter 09781 | loss 1.5899 | loss(rot) 1.0370 | loss(pos) 0.2237 | loss(seq) 0.3292 | grad 4.5597 | lr 0.0010 | time_forward 1.2380 | time_backward 1.4560 |
[2023-09-02 12:16:32,917::train::INFO] [train] Iter 09782 | loss 1.4504 | loss(rot) 0.6613 | loss(pos) 0.2516 | loss(seq) 0.5375 | grad 4.0385 | lr 0.0010 | time_forward 3.9370 | time_backward 5.6750 |
[2023-09-02 12:16:41,536::train::INFO] [train] Iter 09783 | loss 0.7851 | loss(rot) 0.2868 | loss(pos) 0.2454 | loss(seq) 0.2530 | grad 3.5260 | lr 0.0010 | time_forward 3.6270 | time_backward 4.9900 |
[2023-09-02 12:16:50,453::train::INFO] [train] Iter 09784 | loss 1.2288 | loss(rot) 0.8919 | loss(pos) 0.0877 | loss(seq) 0.2493 | grad 5.2400 | lr 0.0010 | time_forward 3.7150 | time_backward 5.1980 |
[2023-09-02 12:16:58,966::train::INFO] [train] Iter 09785 | loss 1.6215 | loss(rot) 1.5190 | loss(pos) 0.0909 | loss(seq) 0.0116 | grad 4.7502 | lr 0.0010 | time_forward 3.5080 | time_backward 5.0010 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.