text stringlengths 56 1.16k |
|---|
[2023-09-02 20:57:24,972::train::INFO] [train] Iter 14181 | loss 1.6313 | loss(rot) 0.8579 | loss(pos) 0.2016 | loss(seq) 0.5717 | grad 3.6147 | lr 0.0010 | time_forward 1.2450 | time_backward 1.4300 |
[2023-09-02 20:57:35,584::train::INFO] [train] Iter 14182 | loss 1.7463 | loss(rot) 0.8728 | loss(pos) 0.2001 | loss(seq) 0.6735 | grad 4.7795 | lr 0.0010 | time_forward 4.3120 | time_backward 6.2950 |
[2023-09-02 20:57:38,371::train::INFO] [train] Iter 14183 | loss 1.1931 | loss(rot) 1.0587 | loss(pos) 0.0990 | loss(seq) 0.0355 | grad 6.4994 | lr 0.0010 | time_forward 1.2940 | time_backward 1.4740 |
[2023-09-02 20:57:40,709::train::INFO] [train] Iter 14184 | loss 1.3140 | loss(rot) 0.1271 | loss(pos) 1.1791 | loss(seq) 0.0078 | grad 5.4427 | lr 0.0010 | time_forward 1.1200 | time_backward 1.2140 |
[2023-09-02 20:57:51,149::train::INFO] [train] Iter 14185 | loss 1.6063 | loss(rot) 0.4005 | loss(pos) 0.8478 | loss(seq) 0.3580 | grad 4.1350 | lr 0.0010 | time_forward 4.2570 | time_backward 6.1800 |
[2023-09-02 20:57:54,566::train::INFO] [train] Iter 14186 | loss 1.5679 | loss(rot) 1.4248 | loss(pos) 0.1424 | loss(seq) 0.0007 | grad 3.5492 | lr 0.0010 | time_forward 1.4290 | time_backward 1.9860 |
[2023-09-02 20:58:04,160::train::INFO] [train] Iter 14187 | loss 1.2716 | loss(rot) 0.7042 | loss(pos) 0.1633 | loss(seq) 0.4041 | grad 4.6534 | lr 0.0010 | time_forward 3.9770 | time_backward 5.6130 |
[2023-09-02 20:58:10,210::train::INFO] [train] Iter 14188 | loss 0.9916 | loss(rot) 0.8258 | loss(pos) 0.1613 | loss(seq) 0.0046 | grad 6.9452 | lr 0.0010 | time_forward 2.4860 | time_backward 3.5600 |
[2023-09-02 20:58:18,494::train::INFO] [train] Iter 14189 | loss 0.9136 | loss(rot) 0.1711 | loss(pos) 0.6922 | loss(seq) 0.0503 | grad 4.5190 | lr 0.0010 | time_forward 3.5530 | time_backward 4.7270 |
[2023-09-02 20:58:28,809::train::INFO] [train] Iter 14190 | loss 2.7052 | loss(rot) 0.0223 | loss(pos) 2.6816 | loss(seq) 0.0014 | grad 9.0220 | lr 0.0010 | time_forward 4.5390 | time_backward 5.7720 |
[2023-09-02 20:58:31,559::train::INFO] [train] Iter 14191 | loss 0.8765 | loss(rot) 0.2758 | loss(pos) 0.3371 | loss(seq) 0.2636 | grad 4.4760 | lr 0.0010 | time_forward 1.2900 | time_backward 1.4570 |
[2023-09-02 20:58:40,863::train::INFO] [train] Iter 14192 | loss 1.1589 | loss(rot) 0.9403 | loss(pos) 0.2102 | loss(seq) 0.0084 | grad 7.5637 | lr 0.0010 | time_forward 3.8410 | time_backward 5.4350 |
[2023-09-02 20:58:51,191::train::INFO] [train] Iter 14193 | loss 1.2250 | loss(rot) 0.5150 | loss(pos) 0.3681 | loss(seq) 0.3419 | grad 3.4500 | lr 0.0010 | time_forward 4.1310 | time_backward 6.1930 |
[2023-09-02 20:58:59,976::train::INFO] [train] Iter 14194 | loss 0.7563 | loss(rot) 0.2032 | loss(pos) 0.4688 | loss(seq) 0.0843 | grad 3.4873 | lr 0.0010 | time_forward 3.6500 | time_backward 5.1320 |
[2023-09-02 20:59:02,712::train::INFO] [train] Iter 14195 | loss 2.6519 | loss(rot) 0.2182 | loss(pos) 2.4329 | loss(seq) 0.0007 | grad 6.4784 | lr 0.0010 | time_forward 1.2690 | time_backward 1.4640 |
[2023-09-02 20:59:12,060::train::INFO] [train] Iter 14196 | loss 1.0723 | loss(rot) 0.6764 | loss(pos) 0.1637 | loss(seq) 0.2322 | grad 4.8632 | lr 0.0010 | time_forward 3.9860 | time_backward 5.3590 |
[2023-09-02 20:59:21,812::train::INFO] [train] Iter 14197 | loss 1.4465 | loss(rot) 0.2891 | loss(pos) 0.5856 | loss(seq) 0.5719 | grad 5.6715 | lr 0.0010 | time_forward 4.1540 | time_backward 5.5940 |
[2023-09-02 20:59:29,774::train::INFO] [train] Iter 14198 | loss 0.7675 | loss(rot) 0.2466 | loss(pos) 0.4332 | loss(seq) 0.0877 | grad 4.4176 | lr 0.0010 | time_forward 3.4360 | time_backward 4.5220 |
[2023-09-02 20:59:38,488::train::INFO] [train] Iter 14199 | loss 2.3092 | loss(rot) 1.9951 | loss(pos) 0.1221 | loss(seq) 0.1919 | grad 6.8007 | lr 0.0010 | time_forward 3.6240 | time_backward 5.0880 |
[2023-09-02 20:59:48,960::train::INFO] [train] Iter 14200 | loss 0.7598 | loss(rot) 0.3015 | loss(pos) 0.2881 | loss(seq) 0.1702 | grad 4.7237 | lr 0.0010 | time_forward 4.2000 | time_backward 6.2680 |
[2023-09-02 20:59:51,274::train::INFO] [train] Iter 14201 | loss 0.9491 | loss(rot) 0.1859 | loss(pos) 0.2399 | loss(seq) 0.5233 | grad 4.2297 | lr 0.0010 | time_forward 1.1040 | time_backward 1.2070 |
[2023-09-02 21:00:00,452::train::INFO] [train] Iter 14202 | loss 1.5339 | loss(rot) 0.0844 | loss(pos) 0.8607 | loss(seq) 0.5888 | grad 5.0535 | lr 0.0010 | time_forward 3.7630 | time_backward 5.3800 |
[2023-09-02 21:00:10,376::train::INFO] [train] Iter 14203 | loss 0.6547 | loss(rot) 0.2181 | loss(pos) 0.2453 | loss(seq) 0.1913 | grad 5.0710 | lr 0.0010 | time_forward 4.0580 | time_backward 5.8620 |
[2023-09-02 21:00:21,035::train::INFO] [train] Iter 14204 | loss 1.8083 | loss(rot) 1.4642 | loss(pos) 0.1305 | loss(seq) 0.2136 | grad 6.2536 | lr 0.0010 | time_forward 4.3030 | time_backward 6.3530 |
[2023-09-02 21:00:31,866::train::INFO] [train] Iter 14205 | loss 1.6883 | loss(rot) 1.4943 | loss(pos) 0.1939 | loss(seq) 0.0001 | grad 5.4466 | lr 0.0010 | time_forward 4.4830 | time_backward 6.3440 |
[2023-09-02 21:00:38,895::train::INFO] [train] Iter 14206 | loss 1.6845 | loss(rot) 1.0115 | loss(pos) 0.1549 | loss(seq) 0.5181 | grad 4.7648 | lr 0.0010 | time_forward 3.0470 | time_backward 3.9780 |
[2023-09-02 21:00:49,422::train::INFO] [train] Iter 14207 | loss 1.4149 | loss(rot) 0.8928 | loss(pos) 0.1314 | loss(seq) 0.3908 | grad 3.4382 | lr 0.0010 | time_forward 4.4560 | time_backward 6.0670 |
[2023-09-02 21:00:59,755::train::INFO] [train] Iter 14208 | loss 0.9954 | loss(rot) 0.8581 | loss(pos) 0.1230 | loss(seq) 0.0143 | grad 5.6214 | lr 0.0010 | time_forward 4.0950 | time_backward 6.2350 |
[2023-09-02 21:01:02,562::train::INFO] [train] Iter 14209 | loss 1.4702 | loss(rot) 0.9902 | loss(pos) 0.2445 | loss(seq) 0.2355 | grad 6.5345 | lr 0.0010 | time_forward 1.2660 | time_backward 1.5360 |
[2023-09-02 21:01:12,755::train::INFO] [train] Iter 14210 | loss 1.0365 | loss(rot) 0.4796 | loss(pos) 0.5019 | loss(seq) 0.0550 | grad 4.3840 | lr 0.0010 | time_forward 4.1920 | time_backward 5.9970 |
[2023-09-02 21:01:22,014::train::INFO] [train] Iter 14211 | loss 1.0819 | loss(rot) 0.6096 | loss(pos) 0.1375 | loss(seq) 0.3348 | grad 5.2702 | lr 0.0010 | time_forward 3.9200 | time_backward 5.3250 |
[2023-09-02 21:01:24,812::train::INFO] [train] Iter 14212 | loss 0.9540 | loss(rot) 0.3299 | loss(pos) 0.2696 | loss(seq) 0.3545 | grad 4.2119 | lr 0.0010 | time_forward 1.2880 | time_backward 1.5060 |
[2023-09-02 21:01:28,317::train::INFO] [train] Iter 14213 | loss 2.3156 | loss(rot) 1.9780 | loss(pos) 0.2180 | loss(seq) 0.1195 | grad 4.5761 | lr 0.0010 | time_forward 1.4990 | time_backward 2.0020 |
[2023-09-02 21:01:39,878::train::INFO] [train] Iter 14214 | loss 1.7538 | loss(rot) 0.2244 | loss(pos) 1.5225 | loss(seq) 0.0069 | grad 4.1216 | lr 0.0010 | time_forward 4.8680 | time_backward 6.6900 |
[2023-09-02 21:01:48,740::train::INFO] [train] Iter 14215 | loss 1.4305 | loss(rot) 0.7142 | loss(pos) 0.2093 | loss(seq) 0.5070 | grad 3.1302 | lr 0.0010 | time_forward 3.6580 | time_backward 5.2000 |
[2023-09-02 21:01:51,504::train::INFO] [train] Iter 14216 | loss 2.3215 | loss(rot) 2.1564 | loss(pos) 0.0774 | loss(seq) 0.0877 | grad 5.7343 | lr 0.0010 | time_forward 1.2680 | time_backward 1.4920 |
[2023-09-02 21:01:53,820::train::INFO] [train] Iter 14217 | loss 1.4160 | loss(rot) 0.9318 | loss(pos) 0.1388 | loss(seq) 0.3454 | grad 4.6465 | lr 0.0010 | time_forward 1.0700 | time_backward 1.2420 |
[2023-09-02 21:01:56,618::train::INFO] [train] Iter 14218 | loss 2.1454 | loss(rot) 1.8631 | loss(pos) 0.2363 | loss(seq) 0.0460 | grad 3.0341 | lr 0.0010 | time_forward 1.2990 | time_backward 1.4940 |
[2023-09-02 21:02:07,078::train::INFO] [train] Iter 14219 | loss 1.6004 | loss(rot) 1.3533 | loss(pos) 0.2224 | loss(seq) 0.0247 | grad 7.4813 | lr 0.0010 | time_forward 4.1780 | time_backward 6.2790 |
[2023-09-02 21:02:09,829::train::INFO] [train] Iter 14220 | loss 1.0404 | loss(rot) 0.6554 | loss(pos) 0.1742 | loss(seq) 0.2108 | grad 5.2193 | lr 0.0010 | time_forward 1.2730 | time_backward 1.4740 |
[2023-09-02 21:02:19,282::train::INFO] [train] Iter 14221 | loss 1.8759 | loss(rot) 1.4343 | loss(pos) 0.1243 | loss(seq) 0.3174 | grad 9.3138 | lr 0.0010 | time_forward 3.9630 | time_backward 5.4860 |
[2023-09-02 21:02:28,666::train::INFO] [train] Iter 14222 | loss 1.5554 | loss(rot) 1.3867 | loss(pos) 0.1570 | loss(seq) 0.0117 | grad 7.7018 | lr 0.0010 | time_forward 3.9850 | time_backward 5.3960 |
[2023-09-02 21:02:31,447::train::INFO] [train] Iter 14223 | loss 2.2404 | loss(rot) 1.9889 | loss(pos) 0.0945 | loss(seq) 0.1570 | grad 5.5278 | lr 0.0010 | time_forward 1.3040 | time_backward 1.4730 |
[2023-09-02 21:02:38,589::train::INFO] [train] Iter 14224 | loss 1.2939 | loss(rot) 0.7787 | loss(pos) 0.1151 | loss(seq) 0.4000 | grad 8.2858 | lr 0.0010 | time_forward 3.0680 | time_backward 4.0700 |
[2023-09-02 21:02:49,033::train::INFO] [train] Iter 14225 | loss 0.9871 | loss(rot) 0.2669 | loss(pos) 0.4985 | loss(seq) 0.2217 | grad 3.4944 | lr 0.0010 | time_forward 4.3210 | time_backward 6.1200 |
[2023-09-02 21:02:59,880::train::INFO] [train] Iter 14226 | loss 1.5560 | loss(rot) 0.9681 | loss(pos) 0.1522 | loss(seq) 0.4357 | grad 4.3286 | lr 0.0010 | time_forward 4.3900 | time_backward 6.4530 |
[2023-09-02 21:03:09,019::train::INFO] [train] Iter 14227 | loss 0.5671 | loss(rot) 0.4801 | loss(pos) 0.0475 | loss(seq) 0.0395 | grad 3.9057 | lr 0.0010 | time_forward 3.8600 | time_backward 5.2750 |
[2023-09-02 21:03:19,049::train::INFO] [train] Iter 14228 | loss 1.2917 | loss(rot) 0.2853 | loss(pos) 0.9660 | loss(seq) 0.0404 | grad 4.8162 | lr 0.0010 | time_forward 4.0880 | time_backward 5.9390 |
[2023-09-02 21:03:27,017::train::INFO] [train] Iter 14229 | loss 0.3826 | loss(rot) 0.1298 | loss(pos) 0.1856 | loss(seq) 0.0672 | grad 2.6146 | lr 0.0010 | time_forward 3.2550 | time_backward 4.7100 |
[2023-09-02 21:03:36,044::train::INFO] [train] Iter 14230 | loss 1.1587 | loss(rot) 0.9960 | loss(pos) 0.1609 | loss(seq) 0.0019 | grad 4.4932 | lr 0.0010 | time_forward 3.8100 | time_backward 5.2130 |
[2023-09-02 21:03:45,379::train::INFO] [train] Iter 14231 | loss 1.4721 | loss(rot) 0.3542 | loss(pos) 0.6135 | loss(seq) 0.5045 | grad 3.8851 | lr 0.0010 | time_forward 3.8780 | time_backward 5.4530 |
[2023-09-02 21:03:48,135::train::INFO] [train] Iter 14232 | loss 1.6413 | loss(rot) 0.5539 | loss(pos) 0.3239 | loss(seq) 0.7634 | grad 3.9926 | lr 0.0010 | time_forward 1.2680 | time_backward 1.4850 |
[2023-09-02 21:03:50,998::train::INFO] [train] Iter 14233 | loss 1.5148 | loss(rot) 0.9398 | loss(pos) 0.1276 | loss(seq) 0.4474 | grad 8.7161 | lr 0.0010 | time_forward 1.3630 | time_backward 1.4960 |
[2023-09-02 21:03:53,813::train::INFO] [train] Iter 14234 | loss 1.2119 | loss(rot) 0.5242 | loss(pos) 0.2916 | loss(seq) 0.3961 | grad 5.2373 | lr 0.0010 | time_forward 1.2990 | time_backward 1.5120 |
[2023-09-02 21:04:04,433::train::INFO] [train] Iter 14235 | loss 1.8066 | loss(rot) 1.6854 | loss(pos) 0.1180 | loss(seq) 0.0032 | grad 4.6827 | lr 0.0010 | time_forward 4.2300 | time_backward 6.3860 |
[2023-09-02 21:04:13,171::train::INFO] [train] Iter 14236 | loss 1.2903 | loss(rot) 0.7754 | loss(pos) 0.1361 | loss(seq) 0.3787 | grad 4.0288 | lr 0.0010 | time_forward 3.6910 | time_backward 5.0430 |
[2023-09-02 21:04:22,842::train::INFO] [train] Iter 14237 | loss 1.1175 | loss(rot) 0.5005 | loss(pos) 0.1994 | loss(seq) 0.4176 | grad 4.8040 | lr 0.0010 | time_forward 4.0190 | time_backward 5.6480 |
[2023-09-02 21:04:33,127::train::INFO] [train] Iter 14238 | loss 0.8559 | loss(rot) 0.2976 | loss(pos) 0.2769 | loss(seq) 0.2813 | grad 3.8573 | lr 0.0010 | time_forward 4.1520 | time_backward 6.1310 |
[2023-09-02 21:04:43,150::train::INFO] [train] Iter 14239 | loss 1.5833 | loss(rot) 0.5130 | loss(pos) 0.6950 | loss(seq) 0.3753 | grad 4.6975 | lr 0.0010 | time_forward 4.3310 | time_backward 5.6730 |
[2023-09-02 21:04:53,793::train::INFO] [train] Iter 14240 | loss 2.3864 | loss(rot) 2.0349 | loss(pos) 0.1461 | loss(seq) 0.2054 | grad 3.5638 | lr 0.0010 | time_forward 4.6320 | time_backward 6.0070 |
[2023-09-02 21:04:56,512::train::INFO] [train] Iter 14241 | loss 1.2545 | loss(rot) 0.4745 | loss(pos) 0.3228 | loss(seq) 0.4572 | grad 3.7431 | lr 0.0010 | time_forward 1.2930 | time_backward 1.4230 |
[2023-09-02 21:05:05,986::train::INFO] [train] Iter 14242 | loss 1.9526 | loss(rot) 1.4946 | loss(pos) 0.1287 | loss(seq) 0.3293 | grad 4.3127 | lr 0.0010 | time_forward 4.0720 | time_backward 5.3990 |
[2023-09-02 21:05:08,681::train::INFO] [train] Iter 14243 | loss 1.3826 | loss(rot) 1.1488 | loss(pos) 0.0677 | loss(seq) 0.1661 | grad 7.9557 | lr 0.0010 | time_forward 1.2680 | time_backward 1.4230 |
[2023-09-02 21:05:12,378::train::INFO] [train] Iter 14244 | loss 2.5821 | loss(rot) 1.8582 | loss(pos) 0.2754 | loss(seq) 0.4485 | grad 6.1212 | lr 0.0010 | time_forward 1.5870 | time_backward 2.1070 |
[2023-09-02 21:05:14,605::train::INFO] [train] Iter 14245 | loss 1.1996 | loss(rot) 1.0200 | loss(pos) 0.1108 | loss(seq) 0.0687 | grad 8.0369 | lr 0.0010 | time_forward 1.0450 | time_backward 1.1800 |
[2023-09-02 21:05:17,312::train::INFO] [train] Iter 14246 | loss 1.1386 | loss(rot) 0.5232 | loss(pos) 0.1856 | loss(seq) 0.4297 | grad 3.8890 | lr 0.0010 | time_forward 1.2740 | time_backward 1.4290 |
[2023-09-02 21:05:28,112::train::INFO] [train] Iter 14247 | loss 0.6989 | loss(rot) 0.2733 | loss(pos) 0.3557 | loss(seq) 0.0700 | grad 3.8811 | lr 0.0010 | time_forward 4.2840 | time_backward 6.5120 |
[2023-09-02 21:05:36,552::train::INFO] [train] Iter 14248 | loss 1.3497 | loss(rot) 0.8618 | loss(pos) 0.1222 | loss(seq) 0.3657 | grad 5.4998 | lr 0.0010 | time_forward 3.4890 | time_backward 4.9490 |
[2023-09-02 21:05:45,364::train::INFO] [train] Iter 14249 | loss 2.0887 | loss(rot) 1.9974 | loss(pos) 0.0901 | loss(seq) 0.0012 | grad 3.8140 | lr 0.0010 | time_forward 3.6980 | time_backward 5.1100 |
[2023-09-02 21:05:54,070::train::INFO] [train] Iter 14250 | loss 1.6557 | loss(rot) 0.0433 | loss(pos) 1.6116 | loss(seq) 0.0008 | grad 5.9737 | lr 0.0010 | time_forward 3.6990 | time_backward 5.0040 |
[2023-09-02 21:06:02,780::train::INFO] [train] Iter 14251 | loss 1.4887 | loss(rot) 0.7848 | loss(pos) 0.2086 | loss(seq) 0.4954 | grad 3.4214 | lr 0.0010 | time_forward 3.6140 | time_backward 5.0930 |
[2023-09-02 21:06:10,668::train::INFO] [train] Iter 14252 | loss 0.9528 | loss(rot) 0.0801 | loss(pos) 0.8633 | loss(seq) 0.0094 | grad 5.0696 | lr 0.0010 | time_forward 3.1930 | time_backward 4.6910 |
[2023-09-02 21:06:18,523::train::INFO] [train] Iter 14253 | loss 2.1589 | loss(rot) 2.0931 | loss(pos) 0.0487 | loss(seq) 0.0171 | grad 5.5100 | lr 0.0010 | time_forward 3.2170 | time_backward 4.6350 |
[2023-09-02 21:06:20,868::train::INFO] [train] Iter 14254 | loss 1.0867 | loss(rot) 0.4033 | loss(pos) 0.2599 | loss(seq) 0.4236 | grad 3.6007 | lr 0.0010 | time_forward 1.1050 | time_backward 1.2370 |
[2023-09-02 21:06:30,670::train::INFO] [train] Iter 14255 | loss 1.0524 | loss(rot) 0.1743 | loss(pos) 0.4825 | loss(seq) 0.3957 | grad 2.8062 | lr 0.0010 | time_forward 4.1320 | time_backward 5.6650 |
[2023-09-02 21:06:41,074::train::INFO] [train] Iter 14256 | loss 2.0394 | loss(rot) 1.9283 | loss(pos) 0.1079 | loss(seq) 0.0032 | grad 4.2135 | lr 0.0010 | time_forward 4.2100 | time_backward 6.1900 |
[2023-09-02 21:06:48,942::train::INFO] [train] Iter 14257 | loss 1.3098 | loss(rot) 0.6310 | loss(pos) 0.2643 | loss(seq) 0.4145 | grad 3.6350 | lr 0.0010 | time_forward 3.2620 | time_backward 4.6020 |
[2023-09-02 21:06:59,474::train::INFO] [train] Iter 14258 | loss 1.7410 | loss(rot) 1.6960 | loss(pos) 0.0449 | loss(seq) 0.0000 | grad 14.5451 | lr 0.0010 | time_forward 4.1730 | time_backward 6.3560 |
[2023-09-02 21:07:02,327::train::INFO] [train] Iter 14259 | loss 2.3040 | loss(rot) 1.6666 | loss(pos) 0.2074 | loss(seq) 0.4299 | grad 4.5443 | lr 0.0010 | time_forward 1.3020 | time_backward 1.5470 |
[2023-09-02 21:07:11,644::train::INFO] [train] Iter 14260 | loss 1.9180 | loss(rot) 1.2071 | loss(pos) 0.1865 | loss(seq) 0.5243 | grad 4.9296 | lr 0.0010 | time_forward 3.8510 | time_backward 5.4630 |
[2023-09-02 21:07:14,449::train::INFO] [train] Iter 14261 | loss 1.8795 | loss(rot) 1.2336 | loss(pos) 0.1670 | loss(seq) 0.4789 | grad 7.2119 | lr 0.0010 | time_forward 1.2790 | time_backward 1.5020 |
[2023-09-02 21:07:23,135::train::INFO] [train] Iter 14262 | loss 1.3580 | loss(rot) 1.2602 | loss(pos) 0.0916 | loss(seq) 0.0062 | grad 5.8757 | lr 0.0010 | time_forward 3.6730 | time_backward 5.0100 |
[2023-09-02 21:07:32,675::train::INFO] [train] Iter 14263 | loss 0.8816 | loss(rot) 0.2139 | loss(pos) 0.6568 | loss(seq) 0.0109 | grad 4.3902 | lr 0.0010 | time_forward 3.9900 | time_backward 5.5470 |
[2023-09-02 21:07:44,810::train::INFO] [train] Iter 14264 | loss 3.0084 | loss(rot) 2.7210 | loss(pos) 0.1724 | loss(seq) 0.1150 | grad 4.1694 | lr 0.0010 | time_forward 4.9150 | time_backward 7.2160 |
[2023-09-02 21:07:47,262::train::INFO] [train] Iter 14265 | loss 1.4198 | loss(rot) 0.7710 | loss(pos) 0.1080 | loss(seq) 0.5408 | grad 3.6410 | lr 0.0010 | time_forward 1.1390 | time_backward 1.3100 |
[2023-09-02 21:07:57,292::train::INFO] [train] Iter 14266 | loss 1.9562 | loss(rot) 1.3961 | loss(pos) 0.2643 | loss(seq) 0.2958 | grad 6.6055 | lr 0.0010 | time_forward 4.1870 | time_backward 5.8400 |
[2023-09-02 21:08:01,064::train::INFO] [train] Iter 14267 | loss 1.5956 | loss(rot) 0.9760 | loss(pos) 0.2937 | loss(seq) 0.3258 | grad 3.5051 | lr 0.0010 | time_forward 1.5630 | time_backward 2.2040 |
[2023-09-02 21:08:04,043::train::INFO] [train] Iter 14268 | loss 1.6508 | loss(rot) 1.5056 | loss(pos) 0.1446 | loss(seq) 0.0007 | grad 6.2693 | lr 0.0010 | time_forward 1.3960 | time_backward 1.5790 |
[2023-09-02 21:08:06,559::train::INFO] [train] Iter 14269 | loss 1.3300 | loss(rot) 0.4839 | loss(pos) 0.2305 | loss(seq) 0.6156 | grad 4.1348 | lr 0.0010 | time_forward 1.1310 | time_backward 1.3810 |
[2023-09-02 21:08:16,355::train::INFO] [train] Iter 14270 | loss 1.1077 | loss(rot) 0.2996 | loss(pos) 0.2791 | loss(seq) 0.5289 | grad 3.9745 | lr 0.0010 | time_forward 4.1320 | time_backward 5.6290 |
[2023-09-02 21:08:25,875::train::INFO] [train] Iter 14271 | loss 1.7871 | loss(rot) 1.7291 | loss(pos) 0.0520 | loss(seq) 0.0060 | grad 8.1665 | lr 0.0010 | time_forward 3.9190 | time_backward 5.5980 |
[2023-09-02 21:08:35,709::train::INFO] [train] Iter 14272 | loss 0.5693 | loss(rot) 0.2072 | loss(pos) 0.2835 | loss(seq) 0.0785 | grad 2.9011 | lr 0.0010 | time_forward 4.0450 | time_backward 5.7850 |
[2023-09-02 21:08:44,794::train::INFO] [train] Iter 14273 | loss 1.4219 | loss(rot) 1.2542 | loss(pos) 0.1675 | loss(seq) 0.0001 | grad 5.2079 | lr 0.0010 | time_forward 3.8290 | time_backward 5.2530 |
[2023-09-02 21:08:53,106::train::INFO] [train] Iter 14274 | loss 0.8738 | loss(rot) 0.3745 | loss(pos) 0.1338 | loss(seq) 0.3654 | grad 2.7951 | lr 0.0010 | time_forward 3.4490 | time_backward 4.8590 |
[2023-09-02 21:09:02,860::train::INFO] [train] Iter 14275 | loss 1.1872 | loss(rot) 0.5837 | loss(pos) 0.2478 | loss(seq) 0.3557 | grad 3.5478 | lr 0.0010 | time_forward 4.1640 | time_backward 5.5860 |
[2023-09-02 21:09:12,190::train::INFO] [train] Iter 14276 | loss 1.2470 | loss(rot) 0.6143 | loss(pos) 0.1689 | loss(seq) 0.4638 | grad 5.0937 | lr 0.0010 | time_forward 3.9090 | time_backward 5.4180 |
[2023-09-02 21:09:15,043::train::INFO] [train] Iter 14277 | loss 2.0569 | loss(rot) 1.2536 | loss(pos) 0.3543 | loss(seq) 0.4489 | grad 3.9525 | lr 0.0010 | time_forward 1.3250 | time_backward 1.5240 |
[2023-09-02 21:09:25,578::train::INFO] [train] Iter 14278 | loss 2.0633 | loss(rot) 1.2453 | loss(pos) 0.2789 | loss(seq) 0.5391 | grad 3.7043 | lr 0.0010 | time_forward 4.3120 | time_backward 6.2190 |
[2023-09-02 21:09:35,908::train::INFO] [train] Iter 14279 | loss 1.8997 | loss(rot) 1.3757 | loss(pos) 0.1797 | loss(seq) 0.3443 | grad 2.9970 | lr 0.0010 | time_forward 4.2470 | time_backward 6.0800 |
[2023-09-02 21:09:46,528::train::INFO] [train] Iter 14280 | loss 2.1220 | loss(rot) 0.0370 | loss(pos) 2.0797 | loss(seq) 0.0053 | grad 9.9956 | lr 0.0010 | time_forward 4.3420 | time_backward 6.2750 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.