text stringlengths 56 1.16k |
|---|
[2023-10-25 12:46:40,623::train::INFO] [train] Iter 595967 | loss 0.9494 | loss(rot) 0.1089 | loss(pos) 0.5267 | loss(seq) 0.3138 | grad 5.5039 | lr 0.0000 | time_forward 2.2300 | time_backward 2.6780 |
[2023-10-25 12:46:50,702::train::INFO] [train] Iter 595968 | loss 1.0578 | loss(rot) 0.9712 | loss(pos) 0.0406 | loss(seq) 0.0460 | grad 8.4121 | lr 0.0000 | time_forward 4.3710 | time_backward 5.7040 |
[2023-10-25 12:47:00,947::train::INFO] [train] Iter 595969 | loss 0.4409 | loss(rot) 0.2065 | loss(pos) 0.2291 | loss(seq) 0.0053 | grad 5.3886 | lr 0.0000 | time_forward 3.9980 | time_backward 6.1580 |
[2023-10-25 12:47:08,513::train::INFO] [train] Iter 595970 | loss 0.5408 | loss(rot) 0.1716 | loss(pos) 0.0935 | loss(seq) 0.2756 | grad 4.5799 | lr 0.0000 | time_forward 3.2510 | time_backward 4.3120 |
[2023-10-25 12:47:17,574::train::INFO] [train] Iter 595971 | loss 0.5377 | loss(rot) 0.0825 | loss(pos) 0.0471 | loss(seq) 0.4081 | grad 2.7274 | lr 0.0000 | time_forward 3.8300 | time_backward 5.2270 |
[2023-10-25 12:47:27,798::train::INFO] [train] Iter 595972 | loss 2.7238 | loss(rot) 2.3353 | loss(pos) 0.1445 | loss(seq) 0.2440 | grad 7.7784 | lr 0.0000 | time_forward 4.3370 | time_backward 5.8840 |
[2023-10-25 12:47:37,754::train::INFO] [train] Iter 595973 | loss 1.5799 | loss(rot) 1.1887 | loss(pos) 0.0521 | loss(seq) 0.3391 | grad 4.4459 | lr 0.0000 | time_forward 4.0740 | time_backward 5.8790 |
[2023-10-25 12:47:47,744::train::INFO] [train] Iter 595974 | loss 0.1280 | loss(rot) 0.0949 | loss(pos) 0.0330 | loss(seq) 0.0001 | grad 3.5784 | lr 0.0000 | time_forward 4.0870 | time_backward 5.9000 |
[2023-10-25 12:47:56,801::train::INFO] [train] Iter 595975 | loss 0.9929 | loss(rot) 0.1362 | loss(pos) 0.3022 | loss(seq) 0.5545 | grad 3.5311 | lr 0.0000 | time_forward 3.8260 | time_backward 5.2260 |
[2023-10-25 12:47:59,647::train::INFO] [train] Iter 595976 | loss 0.3790 | loss(rot) 0.1062 | loss(pos) 0.2370 | loss(seq) 0.0358 | grad 4.8146 | lr 0.0000 | time_forward 1.3740 | time_backward 1.4700 |
[2023-10-25 12:48:08,789::train::INFO] [train] Iter 595977 | loss 1.4151 | loss(rot) 0.6522 | loss(pos) 0.3814 | loss(seq) 0.3815 | grad 4.2645 | lr 0.0000 | time_forward 3.9390 | time_backward 5.1990 |
[2023-10-25 12:48:12,106::train::INFO] [train] Iter 595978 | loss 1.6481 | loss(rot) 0.9621 | loss(pos) 0.1527 | loss(seq) 0.5334 | grad 3.5993 | lr 0.0000 | time_forward 1.4890 | time_backward 1.8260 |
[2023-10-25 12:48:19,488::train::INFO] [train] Iter 595979 | loss 0.7673 | loss(rot) 0.2757 | loss(pos) 0.3483 | loss(seq) 0.1432 | grad 4.5938 | lr 0.0000 | time_forward 3.1680 | time_backward 4.2090 |
[2023-10-25 12:48:21,658::train::INFO] [train] Iter 595980 | loss 0.3063 | loss(rot) 0.2187 | loss(pos) 0.0185 | loss(seq) 0.0691 | grad 2.9020 | lr 0.0000 | time_forward 0.9850 | time_backward 1.1820 |
[2023-10-25 12:48:24,701::train::INFO] [train] Iter 595981 | loss 0.2691 | loss(rot) 0.1005 | loss(pos) 0.0191 | loss(seq) 0.1495 | grad 1.5567 | lr 0.0000 | time_forward 1.4190 | time_backward 1.6190 |
[2023-10-25 12:48:27,567::train::INFO] [train] Iter 595982 | loss 0.5617 | loss(rot) 0.3134 | loss(pos) 0.0196 | loss(seq) 0.2287 | grad 2.9334 | lr 0.0000 | time_forward 1.3980 | time_backward 1.4640 |
[2023-10-25 12:48:30,393::train::INFO] [train] Iter 595983 | loss 0.8592 | loss(rot) 0.2037 | loss(pos) 0.0561 | loss(seq) 0.5994 | grad 3.1053 | lr 0.0000 | time_forward 1.3750 | time_backward 1.4480 |
[2023-10-25 12:48:33,276::train::INFO] [train] Iter 595984 | loss 0.8910 | loss(rot) 0.8702 | loss(pos) 0.0146 | loss(seq) 0.0062 | grad 7.9865 | lr 0.0000 | time_forward 1.3700 | time_backward 1.5090 |
[2023-10-25 12:48:36,770::train::INFO] [train] Iter 595985 | loss 1.0568 | loss(rot) 0.5657 | loss(pos) 0.1579 | loss(seq) 0.3332 | grad 5.5543 | lr 0.0000 | time_forward 1.5490 | time_backward 1.9430 |
[2023-10-25 12:48:46,931::train::INFO] [train] Iter 595986 | loss 0.8812 | loss(rot) 0.0170 | loss(pos) 0.8581 | loss(seq) 0.0061 | grad 8.9237 | lr 0.0000 | time_forward 4.0550 | time_backward 6.1020 |
[2023-10-25 12:48:53,397::train::INFO] [train] Iter 595987 | loss 0.9215 | loss(rot) 0.8472 | loss(pos) 0.0229 | loss(seq) 0.0514 | grad 4.4235 | lr 0.0000 | time_forward 2.7110 | time_backward 3.7520 |
[2023-10-25 12:49:03,416::train::INFO] [train] Iter 595988 | loss 0.7558 | loss(rot) 0.5971 | loss(pos) 0.0455 | loss(seq) 0.1132 | grad 9.5507 | lr 0.0000 | time_forward 4.1950 | time_backward 5.8050 |
[2023-10-25 12:49:12,136::train::INFO] [train] Iter 595989 | loss 1.6737 | loss(rot) 1.0076 | loss(pos) 0.2940 | loss(seq) 0.3722 | grad 4.7393 | lr 0.0000 | time_forward 3.7030 | time_backward 5.0140 |
[2023-10-25 12:49:22,112::train::INFO] [train] Iter 595990 | loss 0.7093 | loss(rot) 0.5497 | loss(pos) 0.0371 | loss(seq) 0.1225 | grad 2.8499 | lr 0.0000 | time_forward 4.1780 | time_backward 5.7950 |
[2023-10-25 12:49:24,820::train::INFO] [train] Iter 595991 | loss 1.4067 | loss(rot) 1.2026 | loss(pos) 0.0365 | loss(seq) 0.1675 | grad 16.1823 | lr 0.0000 | time_forward 1.2680 | time_backward 1.4370 |
[2023-10-25 12:49:27,641::train::INFO] [train] Iter 595992 | loss 0.2226 | loss(rot) 0.1458 | loss(pos) 0.0156 | loss(seq) 0.0612 | grad 1.7713 | lr 0.0000 | time_forward 1.3750 | time_backward 1.4420 |
[2023-10-25 12:49:37,759::train::INFO] [train] Iter 595993 | loss 1.8955 | loss(rot) 1.4496 | loss(pos) 0.0758 | loss(seq) 0.3702 | grad 2.4222 | lr 0.0000 | time_forward 4.1940 | time_backward 5.8790 |
[2023-10-25 12:49:47,101::train::INFO] [train] Iter 595994 | loss 0.2946 | loss(rot) 0.2550 | loss(pos) 0.0190 | loss(seq) 0.0206 | grad 2.4649 | lr 0.0000 | time_forward 3.9590 | time_backward 5.3810 |
[2023-10-25 12:49:55,119::train::INFO] [train] Iter 595995 | loss 2.9497 | loss(rot) 2.3760 | loss(pos) 0.1045 | loss(seq) 0.4692 | grad 6.5929 | lr 0.0000 | time_forward 3.3510 | time_backward 4.6630 |
[2023-10-25 12:50:03,257::train::INFO] [train] Iter 595996 | loss 1.7397 | loss(rot) 0.7908 | loss(pos) 0.3595 | loss(seq) 0.5894 | grad 6.0465 | lr 0.0000 | time_forward 3.3540 | time_backward 4.7810 |
[2023-10-25 12:50:12,020::train::INFO] [train] Iter 595997 | loss 1.1765 | loss(rot) 0.6930 | loss(pos) 0.1712 | loss(seq) 0.3122 | grad 3.0524 | lr 0.0000 | time_forward 3.5700 | time_backward 5.1900 |
[2023-10-25 12:50:14,803::train::INFO] [train] Iter 595998 | loss 0.5666 | loss(rot) 0.1948 | loss(pos) 0.0679 | loss(seq) 0.3040 | grad 3.6658 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4520 |
[2023-10-25 12:50:17,612::train::INFO] [train] Iter 595999 | loss 1.0156 | loss(rot) 0.0049 | loss(pos) 1.0104 | loss(seq) 0.0002 | grad 14.7167 | lr 0.0000 | time_forward 1.3130 | time_backward 1.4930 |
[2023-10-25 12:50:27,210::train::INFO] [train] Iter 596000 | loss 0.4135 | loss(rot) 0.3482 | loss(pos) 0.0340 | loss(seq) 0.0313 | grad 38.8732 | lr 0.0000 | time_forward 4.1110 | time_backward 5.4830 |
[2023-10-25 12:51:17,586::train::INFO] [val] Iter 596000 | loss 1.1179 | loss(rot) 0.7424 | loss(pos) 0.2036 | loss(seq) 0.1719 |
[2023-10-25 12:51:31,735::train::INFO] [train] Iter 596001 | loss 0.2612 | loss(rot) 0.0823 | loss(pos) 0.0596 | loss(seq) 0.1192 | grad 3.0122 | lr 0.0000 | time_forward 7.5610 | time_backward 6.1340 |
[2023-10-25 12:51:41,830::train::INFO] [train] Iter 596002 | loss 0.5623 | loss(rot) 0.1590 | loss(pos) 0.1922 | loss(seq) 0.2111 | grad 4.6857 | lr 0.0000 | time_forward 4.1060 | time_backward 5.9850 |
[2023-10-25 12:51:50,323::train::INFO] [train] Iter 596003 | loss 1.4526 | loss(rot) 1.4090 | loss(pos) 0.0435 | loss(seq) 0.0000 | grad 7.5232 | lr 0.0000 | time_forward 3.5960 | time_backward 4.8950 |
[2023-10-25 12:51:52,311::train::INFO] [train] Iter 596004 | loss 0.2141 | loss(rot) 0.1535 | loss(pos) 0.0603 | loss(seq) 0.0003 | grad 2.0113 | lr 0.0000 | time_forward 0.9220 | time_backward 1.0620 |
[2023-10-25 12:52:02,509::train::INFO] [train] Iter 596005 | loss 0.5112 | loss(rot) 0.0626 | loss(pos) 0.4191 | loss(seq) 0.0295 | grad 4.7242 | lr 0.0000 | time_forward 4.1970 | time_backward 5.9940 |
[2023-10-25 12:52:10,637::train::INFO] [train] Iter 596006 | loss 0.8042 | loss(rot) 0.4344 | loss(pos) 0.2422 | loss(seq) 0.1276 | grad 3.9505 | lr 0.0000 | time_forward 3.4820 | time_backward 4.6430 |
[2023-10-25 12:52:19,206::train::INFO] [train] Iter 596007 | loss 0.4554 | loss(rot) 0.3820 | loss(pos) 0.0734 | loss(seq) 0.0000 | grad 2.9905 | lr 0.0000 | time_forward 3.6070 | time_backward 4.9580 |
[2023-10-25 12:52:21,783::train::INFO] [train] Iter 596008 | loss 0.8541 | loss(rot) 0.5111 | loss(pos) 0.1777 | loss(seq) 0.1654 | grad 3.6460 | lr 0.0000 | time_forward 1.2250 | time_backward 1.3490 |
[2023-10-25 12:52:28,573::train::INFO] [train] Iter 596009 | loss 0.9246 | loss(rot) 0.0345 | loss(pos) 0.8857 | loss(seq) 0.0044 | grad 5.7925 | lr 0.0000 | time_forward 2.9390 | time_backward 3.8340 |
[2023-10-25 12:52:31,336::train::INFO] [train] Iter 596010 | loss 0.5813 | loss(rot) 0.1735 | loss(pos) 0.0462 | loss(seq) 0.3616 | grad 2.8571 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4450 |
[2023-10-25 12:52:34,172::train::INFO] [train] Iter 596011 | loss 0.7788 | loss(rot) 0.7313 | loss(pos) 0.0351 | loss(seq) 0.0125 | grad 23.6490 | lr 0.0000 | time_forward 1.3940 | time_backward 1.4380 |
[2023-10-25 12:52:44,442::train::INFO] [train] Iter 596012 | loss 0.4943 | loss(rot) 0.1307 | loss(pos) 0.0598 | loss(seq) 0.3038 | grad 3.0838 | lr 0.0000 | time_forward 4.1510 | time_backward 6.1170 |
[2023-10-25 12:52:47,310::train::INFO] [train] Iter 596013 | loss 0.1851 | loss(rot) 0.0201 | loss(pos) 0.1495 | loss(seq) 0.0155 | grad 6.3656 | lr 0.0000 | time_forward 1.3820 | time_backward 1.4830 |
[2023-10-25 12:52:56,329::train::INFO] [train] Iter 596014 | loss 0.4461 | loss(rot) 0.3289 | loss(pos) 0.0136 | loss(seq) 0.1036 | grad 58.8068 | lr 0.0000 | time_forward 3.9090 | time_backward 5.1070 |
[2023-10-25 12:53:06,479::train::INFO] [train] Iter 596015 | loss 1.3753 | loss(rot) 1.3103 | loss(pos) 0.0427 | loss(seq) 0.0223 | grad 2.4815 | lr 0.0000 | time_forward 4.2330 | time_backward 5.9130 |
[2023-10-25 12:53:16,616::train::INFO] [train] Iter 596016 | loss 0.3554 | loss(rot) 0.0953 | loss(pos) 0.1331 | loss(seq) 0.1270 | grad 3.7609 | lr 0.0000 | time_forward 4.1710 | time_backward 5.9620 |
[2023-10-25 12:53:24,924::train::INFO] [train] Iter 596017 | loss 0.4357 | loss(rot) 0.0550 | loss(pos) 0.3733 | loss(seq) 0.0074 | grad 4.9873 | lr 0.0000 | time_forward 3.4780 | time_backward 4.8280 |
[2023-10-25 12:53:33,247::train::INFO] [train] Iter 596018 | loss 1.5587 | loss(rot) 1.2485 | loss(pos) 0.0892 | loss(seq) 0.2211 | grad 4.9796 | lr 0.0000 | time_forward 3.5110 | time_backward 4.8080 |
[2023-10-25 12:53:36,067::train::INFO] [train] Iter 596019 | loss 0.3723 | loss(rot) 0.0948 | loss(pos) 0.0670 | loss(seq) 0.2105 | grad 3.2297 | lr 0.0000 | time_forward 1.3980 | time_backward 1.4190 |
[2023-10-25 12:53:38,851::train::INFO] [train] Iter 596020 | loss 0.6878 | loss(rot) 0.4091 | loss(pos) 0.0343 | loss(seq) 0.2445 | grad 4.7139 | lr 0.0000 | time_forward 1.3380 | time_backward 1.4430 |
[2023-10-25 12:53:48,748::train::INFO] [train] Iter 596021 | loss 0.4917 | loss(rot) 0.4555 | loss(pos) 0.0362 | loss(seq) 0.0000 | grad 6.1517 | lr 0.0000 | time_forward 4.0790 | time_backward 5.8150 |
[2023-10-25 12:53:56,792::train::INFO] [train] Iter 596022 | loss 0.4356 | loss(rot) 0.0219 | loss(pos) 0.4080 | loss(seq) 0.0058 | grad 7.6056 | lr 0.0000 | time_forward 3.3310 | time_backward 4.7100 |
[2023-10-25 12:54:00,358::train::INFO] [train] Iter 596023 | loss 0.8451 | loss(rot) 0.3643 | loss(pos) 0.1431 | loss(seq) 0.3377 | grad 3.6387 | lr 0.0000 | time_forward 1.5380 | time_backward 2.0260 |
[2023-10-25 12:54:03,507::train::INFO] [train] Iter 596024 | loss 1.6279 | loss(rot) 1.5991 | loss(pos) 0.0288 | loss(seq) 0.0000 | grad 5.1220 | lr 0.0000 | time_forward 1.5180 | time_backward 1.6270 |
[2023-10-25 12:54:12,993::train::INFO] [train] Iter 596025 | loss 0.2835 | loss(rot) 0.2685 | loss(pos) 0.0095 | loss(seq) 0.0055 | grad 3.0240 | lr 0.0000 | time_forward 4.0680 | time_backward 5.4150 |
[2023-10-25 12:54:22,332::train::INFO] [train] Iter 596026 | loss 0.9031 | loss(rot) 0.0123 | loss(pos) 0.8885 | loss(seq) 0.0023 | grad 11.4596 | lr 0.0000 | time_forward 4.0210 | time_backward 5.3160 |
[2023-10-25 12:54:31,480::train::INFO] [train] Iter 596027 | loss 0.2335 | loss(rot) 0.1811 | loss(pos) 0.0452 | loss(seq) 0.0071 | grad 2.5780 | lr 0.0000 | time_forward 3.9060 | time_backward 5.2380 |
[2023-10-25 12:54:41,584::train::INFO] [train] Iter 596028 | loss 0.9942 | loss(rot) 0.6442 | loss(pos) 0.0368 | loss(seq) 0.3132 | grad 3.1424 | lr 0.0000 | time_forward 4.1830 | time_backward 5.9180 |
[2023-10-25 12:54:50,433::train::INFO] [train] Iter 596029 | loss 0.5329 | loss(rot) 0.5042 | loss(pos) 0.0271 | loss(seq) 0.0016 | grad 4.8873 | lr 0.0000 | time_forward 3.7820 | time_backward 5.0630 |
[2023-10-25 12:54:53,415::train::INFO] [train] Iter 596030 | loss 1.6550 | loss(rot) 0.0059 | loss(pos) 1.6484 | loss(seq) 0.0007 | grad 11.3634 | lr 0.0000 | time_forward 1.4440 | time_backward 1.5350 |
[2023-10-25 12:55:03,765::train::INFO] [train] Iter 596031 | loss 0.3349 | loss(rot) 0.1465 | loss(pos) 0.0374 | loss(seq) 0.1511 | grad 2.2605 | lr 0.0000 | time_forward 4.3750 | time_backward 5.9710 |
[2023-10-25 12:55:11,748::train::INFO] [train] Iter 596032 | loss 0.2344 | loss(rot) 0.1836 | loss(pos) 0.0508 | loss(seq) 0.0000 | grad 1.6888 | lr 0.0000 | time_forward 3.3960 | time_backward 4.5830 |
[2023-10-25 12:55:21,215::train::INFO] [train] Iter 596033 | loss 0.3601 | loss(rot) 0.0981 | loss(pos) 0.0518 | loss(seq) 0.2102 | grad 2.5712 | lr 0.0000 | time_forward 4.1100 | time_backward 5.3540 |
[2023-10-25 12:55:31,284::train::INFO] [train] Iter 596034 | loss 0.4013 | loss(rot) 0.3890 | loss(pos) 0.0121 | loss(seq) 0.0003 | grad 3.6238 | lr 0.0000 | time_forward 4.2200 | time_backward 5.8470 |
[2023-10-25 12:55:40,665::train::INFO] [train] Iter 596035 | loss 0.9512 | loss(rot) 0.1453 | loss(pos) 0.5134 | loss(seq) 0.2926 | grad 3.6198 | lr 0.0000 | time_forward 4.0820 | time_backward 5.2950 |
[2023-10-25 12:55:51,062::train::INFO] [train] Iter 596036 | loss 0.2418 | loss(rot) 0.1553 | loss(pos) 0.0390 | loss(seq) 0.0476 | grad 1.9496 | lr 0.0000 | time_forward 4.1730 | time_backward 6.2210 |
[2023-10-25 12:55:59,389::train::INFO] [train] Iter 596037 | loss 0.9380 | loss(rot) 0.6007 | loss(pos) 0.0441 | loss(seq) 0.2932 | grad 2.3255 | lr 0.0000 | time_forward 3.5720 | time_backward 4.7530 |
[2023-10-25 12:56:09,415::train::INFO] [train] Iter 596038 | loss 1.1213 | loss(rot) 0.6997 | loss(pos) 0.1773 | loss(seq) 0.2443 | grad 4.6563 | lr 0.0000 | time_forward 4.1070 | time_backward 5.9150 |
[2023-10-25 12:56:18,836::train::INFO] [train] Iter 596039 | loss 0.9002 | loss(rot) 0.2739 | loss(pos) 0.2957 | loss(seq) 0.3306 | grad 4.6580 | lr 0.0000 | time_forward 4.0550 | time_backward 5.3630 |
[2023-10-25 12:56:27,618::train::INFO] [train] Iter 596040 | loss 0.4679 | loss(rot) 0.1889 | loss(pos) 0.1745 | loss(seq) 0.1046 | grad 3.6868 | lr 0.0000 | time_forward 3.6410 | time_backward 5.1370 |
[2023-10-25 12:56:37,895::train::INFO] [train] Iter 596041 | loss 1.2902 | loss(rot) 1.0126 | loss(pos) 0.0586 | loss(seq) 0.2191 | grad 5.1437 | lr 0.0000 | time_forward 4.2710 | time_backward 6.0030 |
[2023-10-25 12:56:46,446::train::INFO] [train] Iter 596042 | loss 1.4167 | loss(rot) 1.0286 | loss(pos) 0.0402 | loss(seq) 0.3479 | grad 3.3304 | lr 0.0000 | time_forward 3.7120 | time_backward 4.8360 |
[2023-10-25 12:56:49,409::train::INFO] [train] Iter 596043 | loss 1.8017 | loss(rot) 1.7757 | loss(pos) 0.0259 | loss(seq) 0.0001 | grad 3.0163 | lr 0.0000 | time_forward 1.5310 | time_backward 1.4280 |
[2023-10-25 12:56:58,838::train::INFO] [train] Iter 596044 | loss 0.3553 | loss(rot) 0.1271 | loss(pos) 0.1716 | loss(seq) 0.0566 | grad 3.4378 | lr 0.0000 | time_forward 4.0860 | time_backward 5.3190 |
[2023-10-25 12:57:08,907::train::INFO] [train] Iter 596045 | loss 0.2170 | loss(rot) 0.0482 | loss(pos) 0.1540 | loss(seq) 0.0148 | grad 2.9394 | lr 0.0000 | time_forward 4.1720 | time_backward 5.8930 |
[2023-10-25 12:57:17,286::train::INFO] [train] Iter 596046 | loss 1.4398 | loss(rot) 0.8963 | loss(pos) 0.1474 | loss(seq) 0.3960 | grad 3.4678 | lr 0.0000 | time_forward 3.5900 | time_backward 4.7860 |
[2023-10-25 12:57:27,221::train::INFO] [train] Iter 596047 | loss 0.2656 | loss(rot) 0.2201 | loss(pos) 0.0361 | loss(seq) 0.0094 | grad 2.2672 | lr 0.0000 | time_forward 4.0820 | time_backward 5.8500 |
[2023-10-25 12:57:36,033::train::INFO] [train] Iter 596048 | loss 0.4726 | loss(rot) 0.1523 | loss(pos) 0.0616 | loss(seq) 0.2587 | grad 3.2507 | lr 0.0000 | time_forward 3.7320 | time_backward 5.0780 |
[2023-10-25 12:57:45,876::train::INFO] [train] Iter 596049 | loss 0.7771 | loss(rot) 0.7555 | loss(pos) 0.0215 | loss(seq) 0.0000 | grad 3.9175 | lr 0.0000 | time_forward 4.0350 | time_backward 5.8040 |
[2023-10-25 12:57:54,851::train::INFO] [train] Iter 596050 | loss 0.9976 | loss(rot) 0.9191 | loss(pos) 0.0304 | loss(seq) 0.0481 | grad 84.6450 | lr 0.0000 | time_forward 3.7170 | time_backward 5.2560 |
[2023-10-25 12:58:04,089::train::INFO] [train] Iter 596051 | loss 0.5429 | loss(rot) 0.5232 | loss(pos) 0.0191 | loss(seq) 0.0006 | grad 2.1305 | lr 0.0000 | time_forward 3.8500 | time_backward 5.3840 |
[2023-10-25 12:58:12,220::train::INFO] [train] Iter 596052 | loss 0.6184 | loss(rot) 0.4206 | loss(pos) 0.0223 | loss(seq) 0.1756 | grad 7.0778 | lr 0.0000 | time_forward 3.5140 | time_backward 4.6140 |
[2023-10-25 12:58:21,123::train::INFO] [train] Iter 596053 | loss 1.6894 | loss(rot) 0.8553 | loss(pos) 0.1994 | loss(seq) 0.6346 | grad 5.4522 | lr 0.0000 | time_forward 3.8010 | time_backward 5.0980 |
[2023-10-25 12:58:31,059::train::INFO] [train] Iter 596054 | loss 1.4788 | loss(rot) 1.4504 | loss(pos) 0.0265 | loss(seq) 0.0019 | grad 4.8677 | lr 0.0000 | time_forward 4.0210 | time_backward 5.9120 |
[2023-10-25 12:58:41,025::train::INFO] [train] Iter 596055 | loss 0.6423 | loss(rot) 0.2509 | loss(pos) 0.1012 | loss(seq) 0.2902 | grad 3.2803 | lr 0.0000 | time_forward 4.0880 | time_backward 5.8760 |
[2023-10-25 12:58:51,011::train::INFO] [train] Iter 596056 | loss 2.4560 | loss(rot) 0.0238 | loss(pos) 2.4318 | loss(seq) 0.0004 | grad 18.4277 | lr 0.0000 | time_forward 4.0980 | time_backward 5.8840 |
[2023-10-25 12:58:53,751::train::INFO] [train] Iter 596057 | loss 0.8116 | loss(rot) 0.0893 | loss(pos) 0.6857 | loss(seq) 0.0366 | grad 10.2698 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4260 |
[2023-10-25 12:59:03,994::train::INFO] [train] Iter 596058 | loss 0.5678 | loss(rot) 0.3107 | loss(pos) 0.1188 | loss(seq) 0.1383 | grad 3.3230 | lr 0.0000 | time_forward 4.3240 | time_backward 5.9170 |
[2023-10-25 12:59:12,636::train::INFO] [train] Iter 596059 | loss 1.1671 | loss(rot) 1.1002 | loss(pos) 0.0138 | loss(seq) 0.0531 | grad 21.5690 | lr 0.0000 | time_forward 3.6690 | time_backward 4.9690 |
[2023-10-25 12:59:15,155::train::INFO] [train] Iter 596060 | loss 0.8150 | loss(rot) 0.1755 | loss(pos) 0.1554 | loss(seq) 0.4842 | grad 4.1245 | lr 0.0000 | time_forward 1.2210 | time_backward 1.2950 |
[2023-10-25 12:59:23,818::train::INFO] [train] Iter 596061 | loss 1.1273 | loss(rot) 0.7985 | loss(pos) 0.0839 | loss(seq) 0.2450 | grad 4.3038 | lr 0.0000 | time_forward 3.6500 | time_backward 5.0100 |
[2023-10-25 12:59:32,046::train::INFO] [train] Iter 596062 | loss 0.1431 | loss(rot) 0.0454 | loss(pos) 0.0215 | loss(seq) 0.0762 | grad 1.7232 | lr 0.0000 | time_forward 3.4230 | time_backward 4.8030 |
[2023-10-25 12:59:41,944::train::INFO] [train] Iter 596063 | loss 0.4636 | loss(rot) 0.2787 | loss(pos) 0.0390 | loss(seq) 0.1459 | grad 3.7668 | lr 0.0000 | time_forward 4.0550 | time_backward 5.8390 |
[2023-10-25 12:59:52,021::train::INFO] [train] Iter 596064 | loss 0.4342 | loss(rot) 0.1546 | loss(pos) 0.0575 | loss(seq) 0.2220 | grad 3.4629 | lr 0.0000 | time_forward 4.0300 | time_backward 6.0430 |
[2023-10-25 13:00:01,157::train::INFO] [train] Iter 596065 | loss 1.5079 | loss(rot) 0.6993 | loss(pos) 0.3007 | loss(seq) 0.5078 | grad 3.4662 | lr 0.0000 | time_forward 3.8570 | time_backward 5.2760 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.