text stringlengths 56 1.16k |
|---|
[2023-10-23 02:33:41,237::train::INFO] [train] Iter 565097 | loss 0.1777 | loss(rot) 0.1233 | loss(pos) 0.0199 | loss(seq) 0.0345 | grad 1.5560 | lr 0.0000 | time_forward 3.3060 | time_backward 4.5720 |
[2023-10-23 02:33:48,100::train::INFO] [train] Iter 565098 | loss 0.2262 | loss(rot) 0.0885 | loss(pos) 0.0588 | loss(seq) 0.0788 | grad 3.4817 | lr 0.0000 | time_forward 2.9890 | time_backward 3.8710 |
[2023-10-23 02:33:55,921::train::INFO] [train] Iter 565099 | loss 0.4895 | loss(rot) 0.0189 | loss(pos) 0.4636 | loss(seq) 0.0070 | grad 6.5633 | lr 0.0000 | time_forward 3.2190 | time_backward 4.5990 |
[2023-10-23 02:34:02,979::train::INFO] [train] Iter 565100 | loss 0.3312 | loss(rot) 0.2208 | loss(pos) 0.0281 | loss(seq) 0.0823 | grad 3.3788 | lr 0.0000 | time_forward 3.1350 | time_backward 3.9200 |
[2023-10-23 02:34:09,810::train::INFO] [train] Iter 565101 | loss 0.1520 | loss(rot) 0.1047 | loss(pos) 0.0094 | loss(seq) 0.0379 | grad 2.5765 | lr 0.0000 | time_forward 2.9900 | time_backward 3.8390 |
[2023-10-23 02:34:12,857::train::INFO] [train] Iter 565102 | loss 0.5894 | loss(rot) 0.0580 | loss(pos) 0.5295 | loss(seq) 0.0020 | grad 5.6164 | lr 0.0000 | time_forward 1.4060 | time_backward 1.6370 |
[2023-10-23 02:34:20,883::train::INFO] [train] Iter 565103 | loss 1.1180 | loss(rot) 1.0619 | loss(pos) 0.0547 | loss(seq) 0.0014 | grad 4.2081 | lr 0.0000 | time_forward 3.5420 | time_backward 4.4720 |
[2023-10-23 02:34:27,838::train::INFO] [train] Iter 565104 | loss 0.5576 | loss(rot) 0.0885 | loss(pos) 0.4497 | loss(seq) 0.0194 | grad 11.1948 | lr 0.0000 | time_forward 2.9430 | time_backward 4.0100 |
[2023-10-23 02:34:36,080::train::INFO] [train] Iter 565105 | loss 0.4640 | loss(rot) 0.4005 | loss(pos) 0.0456 | loss(seq) 0.0179 | grad 4.1053 | lr 0.0000 | time_forward 3.4680 | time_backward 4.7720 |
[2023-10-23 02:34:42,885::train::INFO] [train] Iter 565106 | loss 0.3613 | loss(rot) 0.3276 | loss(pos) 0.0337 | loss(seq) 0.0000 | grad 4.1926 | lr 0.0000 | time_forward 2.9390 | time_backward 3.8620 |
[2023-10-23 02:34:50,390::train::INFO] [train] Iter 565107 | loss 0.3541 | loss(rot) 0.2381 | loss(pos) 0.0144 | loss(seq) 0.1017 | grad 2.9947 | lr 0.0000 | time_forward 3.3400 | time_backward 4.1610 |
[2023-10-23 02:34:53,438::train::INFO] [train] Iter 565108 | loss 1.5900 | loss(rot) 1.4780 | loss(pos) 0.0226 | loss(seq) 0.0895 | grad 5.1967 | lr 0.0000 | time_forward 1.4050 | time_backward 1.6400 |
[2023-10-23 02:35:01,933::train::INFO] [train] Iter 565109 | loss 1.1176 | loss(rot) 0.5891 | loss(pos) 0.0585 | loss(seq) 0.4700 | grad 7.3952 | lr 0.0000 | time_forward 3.5510 | time_backward 4.9320 |
[2023-10-23 02:35:09,796::train::INFO] [train] Iter 565110 | loss 2.1571 | loss(rot) 1.7343 | loss(pos) 0.0572 | loss(seq) 0.3656 | grad 10.3495 | lr 0.0000 | time_forward 3.6220 | time_backward 4.2390 |
[2023-10-23 02:35:17,369::train::INFO] [train] Iter 565111 | loss 0.1736 | loss(rot) 0.1208 | loss(pos) 0.0346 | loss(seq) 0.0182 | grad 1.9211 | lr 0.0000 | time_forward 3.4180 | time_backward 4.1510 |
[2023-10-23 02:35:25,795::train::INFO] [train] Iter 565112 | loss 0.6072 | loss(rot) 0.1045 | loss(pos) 0.4435 | loss(seq) 0.0592 | grad 5.9366 | lr 0.0000 | time_forward 3.5890 | time_backward 4.8330 |
[2023-10-23 02:35:32,702::train::INFO] [train] Iter 565113 | loss 0.5570 | loss(rot) 0.4734 | loss(pos) 0.0468 | loss(seq) 0.0368 | grad 1.9301 | lr 0.0000 | time_forward 3.0620 | time_backward 3.8420 |
[2023-10-23 02:35:35,497::train::INFO] [train] Iter 565114 | loss 0.2058 | loss(rot) 0.1487 | loss(pos) 0.0293 | loss(seq) 0.0278 | grad 2.4742 | lr 0.0000 | time_forward 1.4030 | time_backward 1.3890 |
[2023-10-23 02:35:38,330::train::INFO] [train] Iter 565115 | loss 0.4859 | loss(rot) 0.2664 | loss(pos) 0.0328 | loss(seq) 0.1867 | grad 5.5235 | lr 0.0000 | time_forward 1.4370 | time_backward 1.3930 |
[2023-10-23 02:35:46,700::train::INFO] [train] Iter 565116 | loss 1.2467 | loss(rot) 1.2065 | loss(pos) 0.0272 | loss(seq) 0.0130 | grad 4.0887 | lr 0.0000 | time_forward 4.0110 | time_backward 4.3560 |
[2023-10-23 02:35:55,399::train::INFO] [train] Iter 565117 | loss 0.9201 | loss(rot) 0.3994 | loss(pos) 0.1913 | loss(seq) 0.3293 | grad 3.5798 | lr 0.0000 | time_forward 3.8890 | time_backward 4.8080 |
[2023-10-23 02:36:02,093::train::INFO] [train] Iter 565118 | loss 2.1286 | loss(rot) 1.7758 | loss(pos) 0.0915 | loss(seq) 0.2614 | grad 3.4605 | lr 0.0000 | time_forward 2.8630 | time_backward 3.8270 |
[2023-10-23 02:36:10,211::train::INFO] [train] Iter 565119 | loss 0.7817 | loss(rot) 0.4532 | loss(pos) 0.0254 | loss(seq) 0.3031 | grad 4.4209 | lr 0.0000 | time_forward 3.6560 | time_backward 4.4580 |
[2023-10-23 02:36:17,359::train::INFO] [train] Iter 565120 | loss 0.4316 | loss(rot) 0.0140 | loss(pos) 0.4072 | loss(seq) 0.0103 | grad 6.3170 | lr 0.0000 | time_forward 3.2680 | time_backward 3.8770 |
[2023-10-23 02:36:24,910::train::INFO] [train] Iter 565121 | loss 0.2453 | loss(rot) 0.1922 | loss(pos) 0.0411 | loss(seq) 0.0120 | grad 2.7242 | lr 0.0000 | time_forward 3.3660 | time_backward 4.1810 |
[2023-10-23 02:36:32,985::train::INFO] [train] Iter 565122 | loss 1.5792 | loss(rot) 0.9847 | loss(pos) 0.1192 | loss(seq) 0.4753 | grad 4.2759 | lr 0.0000 | time_forward 3.3480 | time_backward 4.7240 |
[2023-10-23 02:36:39,532::train::INFO] [train] Iter 565123 | loss 0.4096 | loss(rot) 0.1795 | loss(pos) 0.0245 | loss(seq) 0.2056 | grad 3.4580 | lr 0.0000 | time_forward 2.7920 | time_backward 3.7520 |
[2023-10-23 02:36:46,492::train::INFO] [train] Iter 565124 | loss 0.1924 | loss(rot) 0.0215 | loss(pos) 0.0846 | loss(seq) 0.0862 | grad 3.3493 | lr 0.0000 | time_forward 3.0160 | time_backward 3.9420 |
[2023-10-23 02:36:53,835::train::INFO] [train] Iter 565125 | loss 1.3049 | loss(rot) 1.0668 | loss(pos) 0.0411 | loss(seq) 0.1970 | grad 4.4837 | lr 0.0000 | time_forward 3.2420 | time_backward 4.0970 |
[2023-10-23 02:36:56,508::train::INFO] [train] Iter 565126 | loss 0.4534 | loss(rot) 0.0532 | loss(pos) 0.0234 | loss(seq) 0.3768 | grad 1.9825 | lr 0.0000 | time_forward 1.2570 | time_backward 1.4140 |
[2023-10-23 02:37:02,982::train::INFO] [train] Iter 565127 | loss 0.2908 | loss(rot) 0.1340 | loss(pos) 0.0288 | loss(seq) 0.1280 | grad 2.3079 | lr 0.0000 | time_forward 2.7770 | time_backward 3.6780 |
[2023-10-23 02:37:09,435::train::INFO] [train] Iter 565128 | loss 0.6806 | loss(rot) 0.0127 | loss(pos) 0.6671 | loss(seq) 0.0008 | grad 12.0369 | lr 0.0000 | time_forward 2.7530 | time_backward 3.6970 |
[2023-10-23 02:37:17,388::train::INFO] [train] Iter 565129 | loss 0.2845 | loss(rot) 0.0321 | loss(pos) 0.2474 | loss(seq) 0.0051 | grad 3.8618 | lr 0.0000 | time_forward 3.2580 | time_backward 4.6930 |
[2023-10-23 02:37:22,807::train::INFO] [train] Iter 565130 | loss 0.2033 | loss(rot) 0.1205 | loss(pos) 0.0828 | loss(seq) 0.0000 | grad 1.8633 | lr 0.0000 | time_forward 2.3630 | time_backward 3.0530 |
[2023-10-23 02:37:25,923::train::INFO] [train] Iter 565131 | loss 0.8875 | loss(rot) 0.3842 | loss(pos) 0.0863 | loss(seq) 0.4169 | grad 3.1739 | lr 0.0000 | time_forward 1.3790 | time_backward 1.7330 |
[2023-10-23 02:37:28,645::train::INFO] [train] Iter 565132 | loss 0.8561 | loss(rot) 0.2994 | loss(pos) 0.0670 | loss(seq) 0.4896 | grad 2.9536 | lr 0.0000 | time_forward 1.2950 | time_backward 1.4130 |
[2023-10-23 02:37:35,751::train::INFO] [train] Iter 565133 | loss 2.2786 | loss(rot) 1.9504 | loss(pos) 0.0592 | loss(seq) 0.2691 | grad 4.8746 | lr 0.0000 | time_forward 3.0810 | time_backward 4.0220 |
[2023-10-23 02:37:43,743::train::INFO] [train] Iter 565134 | loss 0.3423 | loss(rot) 0.2928 | loss(pos) 0.0494 | loss(seq) 0.0000 | grad 2.7083 | lr 0.0000 | time_forward 3.5270 | time_backward 4.4620 |
[2023-10-23 02:37:50,799::train::INFO] [train] Iter 565135 | loss 0.3314 | loss(rot) 0.1673 | loss(pos) 0.1182 | loss(seq) 0.0460 | grad 3.7014 | lr 0.0000 | time_forward 3.0640 | time_backward 3.9890 |
[2023-10-23 02:37:53,504::train::INFO] [train] Iter 565136 | loss 0.4530 | loss(rot) 0.0488 | loss(pos) 0.0300 | loss(seq) 0.3741 | grad 1.9013 | lr 0.0000 | time_forward 1.2700 | time_backward 1.4330 |
[2023-10-23 02:38:00,220::train::INFO] [train] Iter 565137 | loss 0.6965 | loss(rot) 0.5497 | loss(pos) 0.0419 | loss(seq) 0.1049 | grad 5.8017 | lr 0.0000 | time_forward 2.8940 | time_backward 3.8180 |
[2023-10-23 02:38:08,227::train::INFO] [train] Iter 565138 | loss 0.3599 | loss(rot) 0.3024 | loss(pos) 0.0575 | loss(seq) 0.0000 | grad 2.6200 | lr 0.0000 | time_forward 3.5680 | time_backward 4.4360 |
[2023-10-23 02:38:10,890::train::INFO] [train] Iter 565139 | loss 1.6982 | loss(rot) 0.9649 | loss(pos) 0.1107 | loss(seq) 0.6226 | grad 4.6226 | lr 0.0000 | time_forward 1.2710 | time_backward 1.3880 |
[2023-10-23 02:38:13,677::train::INFO] [train] Iter 565140 | loss 1.6203 | loss(rot) 1.1459 | loss(pos) 0.0722 | loss(seq) 0.4022 | grad 3.8599 | lr 0.0000 | time_forward 1.2570 | time_backward 1.5270 |
[2023-10-23 02:38:21,625::train::INFO] [train] Iter 565141 | loss 0.6675 | loss(rot) 0.4348 | loss(pos) 0.0319 | loss(seq) 0.2007 | grad 3.0204 | lr 0.0000 | time_forward 3.3370 | time_backward 4.6080 |
[2023-10-23 02:38:29,523::train::INFO] [train] Iter 565142 | loss 0.7556 | loss(rot) 0.5235 | loss(pos) 0.1063 | loss(seq) 0.1258 | grad 3.0647 | lr 0.0000 | time_forward 3.2880 | time_backward 4.6060 |
[2023-10-23 02:38:37,413::train::INFO] [train] Iter 565143 | loss 0.7846 | loss(rot) 0.7392 | loss(pos) 0.0454 | loss(seq) 0.0000 | grad 3.4660 | lr 0.0000 | time_forward 3.2560 | time_backward 4.6320 |
[2023-10-23 02:38:45,298::train::INFO] [train] Iter 565144 | loss 1.3106 | loss(rot) 1.1789 | loss(pos) 0.0549 | loss(seq) 0.0769 | grad 7.6402 | lr 0.0000 | time_forward 3.2350 | time_backward 4.6470 |
[2023-10-23 02:38:53,509::train::INFO] [train] Iter 565145 | loss 0.3152 | loss(rot) 0.2689 | loss(pos) 0.0339 | loss(seq) 0.0124 | grad 3.4630 | lr 0.0000 | time_forward 3.3050 | time_backward 4.9030 |
[2023-10-23 02:39:00,458::train::INFO] [train] Iter 565146 | loss 0.1831 | loss(rot) 0.1609 | loss(pos) 0.0178 | loss(seq) 0.0045 | grad 2.6292 | lr 0.0000 | time_forward 2.9830 | time_backward 3.9620 |
[2023-10-23 02:39:07,469::train::INFO] [train] Iter 565147 | loss 0.3431 | loss(rot) 0.0703 | loss(pos) 0.0611 | loss(seq) 0.2117 | grad 2.4688 | lr 0.0000 | time_forward 2.9830 | time_backward 3.9510 |
[2023-10-23 02:39:09,887::train::INFO] [train] Iter 565148 | loss 1.0105 | loss(rot) 0.6178 | loss(pos) 0.0502 | loss(seq) 0.3426 | grad 5.1287 | lr 0.0000 | time_forward 1.1590 | time_backward 1.2560 |
[2023-10-23 02:39:16,628::train::INFO] [train] Iter 565149 | loss 0.6467 | loss(rot) 0.1215 | loss(pos) 0.0450 | loss(seq) 0.4802 | grad 2.3749 | lr 0.0000 | time_forward 2.9090 | time_backward 3.8290 |
[2023-10-23 02:39:23,161::train::INFO] [train] Iter 565150 | loss 0.8518 | loss(rot) 0.4509 | loss(pos) 0.0187 | loss(seq) 0.3822 | grad 16.8608 | lr 0.0000 | time_forward 2.8470 | time_backward 3.6830 |
[2023-10-23 02:39:25,787::train::INFO] [train] Iter 565151 | loss 0.3117 | loss(rot) 0.1670 | loss(pos) 0.0619 | loss(seq) 0.0828 | grad 3.1249 | lr 0.0000 | time_forward 1.2400 | time_backward 1.3830 |
[2023-10-23 02:39:33,570::train::INFO] [train] Iter 565152 | loss 0.8040 | loss(rot) 0.3278 | loss(pos) 0.3481 | loss(seq) 0.1281 | grad 4.4705 | lr 0.0000 | time_forward 3.4290 | time_backward 4.3510 |
[2023-10-23 02:39:40,798::train::INFO] [train] Iter 565153 | loss 0.2771 | loss(rot) 0.0123 | loss(pos) 0.2624 | loss(seq) 0.0025 | grad 5.5763 | lr 0.0000 | time_forward 3.1270 | time_backward 4.0980 |
[2023-10-23 02:39:46,339::train::INFO] [train] Iter 565154 | loss 1.5755 | loss(rot) 1.0828 | loss(pos) 0.0550 | loss(seq) 0.4377 | grad 6.0547 | lr 0.0000 | time_forward 2.2540 | time_backward 3.2830 |
[2023-10-23 02:39:53,365::train::INFO] [train] Iter 565155 | loss 0.8827 | loss(rot) 0.3221 | loss(pos) 0.3279 | loss(seq) 0.2327 | grad 6.3951 | lr 0.0000 | time_forward 3.0610 | time_backward 3.9540 |
[2023-10-23 02:39:56,057::train::INFO] [train] Iter 565156 | loss 0.4082 | loss(rot) 0.1944 | loss(pos) 0.0473 | loss(seq) 0.1665 | grad 2.7108 | lr 0.0000 | time_forward 1.2750 | time_backward 1.4130 |
[2023-10-23 02:40:03,431::train::INFO] [train] Iter 565157 | loss 1.3570 | loss(rot) 1.3096 | loss(pos) 0.0180 | loss(seq) 0.0293 | grad 4.2099 | lr 0.0000 | time_forward 3.1740 | time_backward 4.1970 |
[2023-10-23 02:40:10,508::train::INFO] [train] Iter 565158 | loss 0.2013 | loss(rot) 0.1616 | loss(pos) 0.0289 | loss(seq) 0.0107 | grad 2.3654 | lr 0.0000 | time_forward 3.0010 | time_backward 4.0730 |
[2023-10-23 02:40:16,633::train::INFO] [train] Iter 565159 | loss 0.3605 | loss(rot) 0.0260 | loss(pos) 0.3313 | loss(seq) 0.0031 | grad 9.2352 | lr 0.0000 | time_forward 2.7190 | time_backward 3.4030 |
[2023-10-23 02:40:23,771::train::INFO] [train] Iter 565160 | loss 2.0273 | loss(rot) 1.1718 | loss(pos) 0.2064 | loss(seq) 0.6491 | grad 6.3273 | lr 0.0000 | time_forward 3.0950 | time_backward 4.0400 |
[2023-10-23 02:40:30,248::train::INFO] [train] Iter 565161 | loss 1.6773 | loss(rot) 0.8549 | loss(pos) 0.5431 | loss(seq) 0.2793 | grad 7.9288 | lr 0.0000 | time_forward 2.7210 | time_backward 3.7530 |
[2023-10-23 02:40:32,902::train::INFO] [train] Iter 565162 | loss 1.2408 | loss(rot) 0.9749 | loss(pos) 0.0347 | loss(seq) 0.2312 | grad 9.4935 | lr 0.0000 | time_forward 1.2580 | time_backward 1.3930 |
[2023-10-23 02:40:40,249::train::INFO] [train] Iter 565163 | loss 0.3591 | loss(rot) 0.2143 | loss(pos) 0.1036 | loss(seq) 0.0412 | grad 3.2642 | lr 0.0000 | time_forward 3.2090 | time_backward 4.1220 |
[2023-10-23 02:40:42,920::train::INFO] [train] Iter 565164 | loss 0.1640 | loss(rot) 0.0947 | loss(pos) 0.0226 | loss(seq) 0.0468 | grad 1.4431 | lr 0.0000 | time_forward 1.2660 | time_backward 1.4020 |
[2023-10-23 02:40:50,202::train::INFO] [train] Iter 565165 | loss 0.6462 | loss(rot) 0.0205 | loss(pos) 0.6200 | loss(seq) 0.0057 | grad 7.3439 | lr 0.0000 | time_forward 3.1750 | time_backward 4.0860 |
[2023-10-23 02:40:56,837::train::INFO] [train] Iter 565166 | loss 0.1111 | loss(rot) 0.0911 | loss(pos) 0.0195 | loss(seq) 0.0005 | grad 1.9503 | lr 0.0000 | time_forward 2.8890 | time_backward 3.7440 |
[2023-10-23 02:40:59,393::train::INFO] [train] Iter 565167 | loss 1.2644 | loss(rot) 0.1971 | loss(pos) 1.0663 | loss(seq) 0.0010 | grad 8.8193 | lr 0.0000 | time_forward 1.1840 | time_backward 1.3680 |
[2023-10-23 02:41:01,961::train::INFO] [train] Iter 565168 | loss 2.2145 | loss(rot) 1.8400 | loss(pos) 0.0568 | loss(seq) 0.3176 | grad 25.4061 | lr 0.0000 | time_forward 1.2020 | time_backward 1.3630 |
[2023-10-23 02:41:04,630::train::INFO] [train] Iter 565169 | loss 0.9844 | loss(rot) 0.7197 | loss(pos) 0.0287 | loss(seq) 0.2360 | grad 4.4785 | lr 0.0000 | time_forward 1.2740 | time_backward 1.3920 |
[2023-10-23 02:41:12,663::train::INFO] [train] Iter 565170 | loss 0.5584 | loss(rot) 0.4652 | loss(pos) 0.0236 | loss(seq) 0.0696 | grad 4.2620 | lr 0.0000 | time_forward 3.2680 | time_backward 4.7450 |
[2023-10-23 02:41:15,383::train::INFO] [train] Iter 565171 | loss 0.7752 | loss(rot) 0.4052 | loss(pos) 0.3498 | loss(seq) 0.0203 | grad 6.3627 | lr 0.0000 | time_forward 1.2890 | time_backward 1.4270 |
[2023-10-23 02:41:23,289::train::INFO] [train] Iter 565172 | loss 0.3568 | loss(rot) 0.1749 | loss(pos) 0.0211 | loss(seq) 0.1608 | grad 2.4833 | lr 0.0000 | time_forward 3.2350 | time_backward 4.6560 |
[2023-10-23 02:41:25,948::train::INFO] [train] Iter 565173 | loss 0.7906 | loss(rot) 0.5308 | loss(pos) 0.0293 | loss(seq) 0.2305 | grad 2.8842 | lr 0.0000 | time_forward 1.2580 | time_backward 1.3970 |
[2023-10-23 02:41:28,684::train::INFO] [train] Iter 565174 | loss 0.4322 | loss(rot) 0.0249 | loss(pos) 0.0534 | loss(seq) 0.3538 | grad 2.9469 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4540 |
[2023-10-23 02:41:34,801::train::INFO] [train] Iter 565175 | loss 1.5074 | loss(rot) 1.4195 | loss(pos) 0.0322 | loss(seq) 0.0557 | grad 4.0685 | lr 0.0000 | time_forward 2.6940 | time_backward 3.4200 |
[2023-10-23 02:41:42,610::train::INFO] [train] Iter 565176 | loss 1.1810 | loss(rot) 1.1527 | loss(pos) 0.0240 | loss(seq) 0.0042 | grad 3.8739 | lr 0.0000 | time_forward 3.1990 | time_backward 4.6070 |
[2023-10-23 02:41:49,039::train::INFO] [train] Iter 565177 | loss 0.3604 | loss(rot) 0.0984 | loss(pos) 0.0204 | loss(seq) 0.2415 | grad 2.3478 | lr 0.0000 | time_forward 2.7800 | time_backward 3.6470 |
[2023-10-23 02:41:52,061::train::INFO] [train] Iter 565178 | loss 0.9653 | loss(rot) 0.8971 | loss(pos) 0.0681 | loss(seq) 0.0001 | grad 24.9985 | lr 0.0000 | time_forward 1.3790 | time_backward 1.6400 |
[2023-10-23 02:41:58,622::train::INFO] [train] Iter 565179 | loss 0.5179 | loss(rot) 0.0670 | loss(pos) 0.4465 | loss(seq) 0.0043 | grad 7.7895 | lr 0.0000 | time_forward 2.8020 | time_backward 3.7430 |
[2023-10-23 02:42:05,260::train::INFO] [train] Iter 565180 | loss 1.5168 | loss(rot) 1.0032 | loss(pos) 0.2316 | loss(seq) 0.2821 | grad 5.6666 | lr 0.0000 | time_forward 2.8190 | time_backward 3.8170 |
[2023-10-23 02:42:07,495::train::INFO] [train] Iter 565181 | loss 0.6794 | loss(rot) 0.2663 | loss(pos) 0.0508 | loss(seq) 0.3623 | grad 2.9983 | lr 0.0000 | time_forward 1.0220 | time_backward 1.2090 |
[2023-10-23 02:42:14,786::train::INFO] [train] Iter 565182 | loss 1.4359 | loss(rot) 1.3612 | loss(pos) 0.0375 | loss(seq) 0.0372 | grad 3.5396 | lr 0.0000 | time_forward 3.1550 | time_backward 4.1340 |
[2023-10-23 02:42:21,937::train::INFO] [train] Iter 565183 | loss 0.5343 | loss(rot) 0.2351 | loss(pos) 0.0381 | loss(seq) 0.2612 | grad 2.7974 | lr 0.0000 | time_forward 2.9800 | time_backward 4.1580 |
[2023-10-23 02:42:25,055::train::INFO] [train] Iter 565184 | loss 1.7285 | loss(rot) 1.5771 | loss(pos) 0.0781 | loss(seq) 0.0733 | grad 4.8565 | lr 0.0000 | time_forward 1.4060 | time_backward 1.7080 |
[2023-10-23 02:42:33,563::train::INFO] [train] Iter 565185 | loss 0.4316 | loss(rot) 0.2144 | loss(pos) 0.1194 | loss(seq) 0.0978 | grad 2.7520 | lr 0.0000 | time_forward 3.6990 | time_backward 4.7960 |
[2023-10-23 02:42:36,247::train::INFO] [train] Iter 565186 | loss 0.8692 | loss(rot) 0.6366 | loss(pos) 0.0518 | loss(seq) 0.1807 | grad 2.6262 | lr 0.0000 | time_forward 1.2540 | time_backward 1.4260 |
[2023-10-23 02:42:39,010::train::INFO] [train] Iter 565187 | loss 0.5850 | loss(rot) 0.3513 | loss(pos) 0.0477 | loss(seq) 0.1860 | grad 3.7321 | lr 0.0000 | time_forward 1.2830 | time_backward 1.4770 |
[2023-10-23 02:42:41,706::train::INFO] [train] Iter 565188 | loss 1.7903 | loss(rot) 1.7119 | loss(pos) 0.0338 | loss(seq) 0.0446 | grad 55.9867 | lr 0.0000 | time_forward 1.2390 | time_backward 1.4520 |
[2023-10-23 02:42:48,932::train::INFO] [train] Iter 565189 | loss 0.4301 | loss(rot) 0.1368 | loss(pos) 0.2779 | loss(seq) 0.0154 | grad 8.1505 | lr 0.0000 | time_forward 3.1780 | time_backward 4.0450 |
[2023-10-23 02:42:51,480::train::INFO] [train] Iter 565190 | loss 0.4019 | loss(rot) 0.1182 | loss(pos) 0.0435 | loss(seq) 0.2402 | grad 2.8717 | lr 0.0000 | time_forward 1.1920 | time_backward 1.3530 |
[2023-10-23 02:42:59,266::train::INFO] [train] Iter 565191 | loss 0.2225 | loss(rot) 0.0678 | loss(pos) 0.1359 | loss(seq) 0.0187 | grad 3.0897 | lr 0.0000 | time_forward 3.3880 | time_backward 4.3960 |
[2023-10-23 02:43:07,218::train::INFO] [train] Iter 565192 | loss 1.4822 | loss(rot) 1.1170 | loss(pos) 0.1186 | loss(seq) 0.2465 | grad 4.5740 | lr 0.0000 | time_forward 3.4630 | time_backward 4.4860 |
[2023-10-23 02:43:14,412::train::INFO] [train] Iter 565193 | loss 0.4606 | loss(rot) 0.0381 | loss(pos) 0.4206 | loss(seq) 0.0019 | grad 7.1219 | lr 0.0000 | time_forward 3.1310 | time_backward 4.0590 |
[2023-10-23 02:43:16,810::train::INFO] [train] Iter 565194 | loss 0.9025 | loss(rot) 0.3797 | loss(pos) 0.1437 | loss(seq) 0.3792 | grad 4.2593 | lr 0.0000 | time_forward 1.1590 | time_backward 1.2310 |
[2023-10-23 02:43:22,645::train::INFO] [train] Iter 565195 | loss 2.2042 | loss(rot) 0.0434 | loss(pos) 2.1577 | loss(seq) 0.0031 | grad 13.3036 | lr 0.0000 | time_forward 2.5590 | time_backward 3.2740 |
[2023-10-23 02:43:24,954::train::INFO] [train] Iter 565196 | loss 0.2683 | loss(rot) 0.1214 | loss(pos) 0.0281 | loss(seq) 0.1187 | grad 1.8157 | lr 0.0000 | time_forward 1.0720 | time_backward 1.2340 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.