text stringlengths 56 1.16k |
|---|
[2023-10-23 02:53:22,921::train::INFO] [train] Iter 565297 | loss 1.8962 | loss(rot) 1.7425 | loss(pos) 0.0417 | loss(seq) 0.1120 | grad 3.8498 | lr 0.0000 | time_forward 2.8470 | time_backward 3.7450 |
[2023-10-23 02:53:30,818::train::INFO] [train] Iter 565298 | loss 0.5688 | loss(rot) 0.2342 | loss(pos) 0.2073 | loss(seq) 0.1273 | grad 4.5184 | lr 0.0000 | time_forward 3.3950 | time_backward 4.5000 |
[2023-10-23 02:53:37,058::train::INFO] [train] Iter 565299 | loss 0.6180 | loss(rot) 0.3795 | loss(pos) 0.0237 | loss(seq) 0.2148 | grad 2.7084 | lr 0.0000 | time_forward 2.6640 | time_backward 3.5730 |
[2023-10-23 02:53:44,009::train::INFO] [train] Iter 565300 | loss 1.0790 | loss(rot) 1.0418 | loss(pos) 0.0213 | loss(seq) 0.0159 | grad 4.3536 | lr 0.0000 | time_forward 3.0170 | time_backward 3.9310 |
[2023-10-23 02:53:46,178::train::INFO] [train] Iter 565301 | loss 1.1140 | loss(rot) 0.7695 | loss(pos) 0.1314 | loss(seq) 0.2132 | grad 5.4686 | lr 0.0000 | time_forward 0.9870 | time_backward 1.1780 |
[2023-10-23 02:53:53,559::train::INFO] [train] Iter 565302 | loss 0.5612 | loss(rot) 0.3719 | loss(pos) 0.0641 | loss(seq) 0.1252 | grad 3.6984 | lr 0.0000 | time_forward 3.2370 | time_backward 4.1410 |
[2023-10-23 02:54:00,321::train::INFO] [train] Iter 565303 | loss 1.2726 | loss(rot) 0.4184 | loss(pos) 0.2213 | loss(seq) 0.6329 | grad 3.9531 | lr 0.0000 | time_forward 2.8980 | time_backward 3.8610 |
[2023-10-23 02:54:07,066::train::INFO] [train] Iter 565304 | loss 0.3279 | loss(rot) 0.1835 | loss(pos) 0.0573 | loss(seq) 0.0871 | grad 2.8830 | lr 0.0000 | time_forward 2.9250 | time_backward 3.8170 |
[2023-10-23 02:54:13,992::train::INFO] [train] Iter 565305 | loss 0.7396 | loss(rot) 0.5926 | loss(pos) 0.0225 | loss(seq) 0.1244 | grad 5.6940 | lr 0.0000 | time_forward 3.0000 | time_backward 3.9220 |
[2023-10-23 02:54:20,960::train::INFO] [train] Iter 565306 | loss 2.2041 | loss(rot) 2.1326 | loss(pos) 0.0343 | loss(seq) 0.0372 | grad 57.8286 | lr 0.0000 | time_forward 3.0100 | time_backward 3.9560 |
[2023-10-23 02:54:29,072::train::INFO] [train] Iter 565307 | loss 1.0126 | loss(rot) 0.0057 | loss(pos) 1.0051 | loss(seq) 0.0017 | grad 13.3110 | lr 0.0000 | time_forward 3.2860 | time_backward 4.8220 |
[2023-10-23 02:54:36,995::train::INFO] [train] Iter 565308 | loss 0.2471 | loss(rot) 0.1002 | loss(pos) 0.0527 | loss(seq) 0.0942 | grad 2.0874 | lr 0.0000 | time_forward 3.2720 | time_backward 4.6490 |
[2023-10-23 02:54:43,174::train::INFO] [train] Iter 565309 | loss 0.5083 | loss(rot) 0.3590 | loss(pos) 0.1411 | loss(seq) 0.0083 | grad 4.5344 | lr 0.0000 | time_forward 2.6720 | time_backward 3.5040 |
[2023-10-23 02:54:45,974::train::INFO] [train] Iter 565310 | loss 0.8078 | loss(rot) 0.0053 | loss(pos) 0.8024 | loss(seq) 0.0002 | grad 16.1714 | lr 0.0000 | time_forward 1.2890 | time_backward 1.5080 |
[2023-10-23 02:54:52,663::train::INFO] [train] Iter 565311 | loss 0.2415 | loss(rot) 0.1055 | loss(pos) 0.0329 | loss(seq) 0.1032 | grad 2.4990 | lr 0.0000 | time_forward 2.9810 | time_backward 3.7060 |
[2023-10-23 02:54:55,291::train::INFO] [train] Iter 565312 | loss 0.5926 | loss(rot) 0.2055 | loss(pos) 0.0276 | loss(seq) 0.3595 | grad 3.4544 | lr 0.0000 | time_forward 1.2470 | time_backward 1.3770 |
[2023-10-23 02:55:02,623::train::INFO] [train] Iter 565313 | loss 0.0855 | loss(rot) 0.0466 | loss(pos) 0.0129 | loss(seq) 0.0260 | grad 1.5051 | lr 0.0000 | time_forward 3.2190 | time_backward 4.1100 |
[2023-10-23 02:55:10,778::train::INFO] [train] Iter 565314 | loss 0.4338 | loss(rot) 0.1296 | loss(pos) 0.2289 | loss(seq) 0.0753 | grad 4.1646 | lr 0.0000 | time_forward 3.3310 | time_backward 4.8200 |
[2023-10-23 02:55:18,533::train::INFO] [train] Iter 565315 | loss 1.3101 | loss(rot) 0.0238 | loss(pos) 1.2816 | loss(seq) 0.0047 | grad 8.9169 | lr 0.0000 | time_forward 3.3680 | time_backward 4.3840 |
[2023-10-23 02:55:23,818::train::INFO] [train] Iter 565316 | loss 0.2850 | loss(rot) 0.0638 | loss(pos) 0.1481 | loss(seq) 0.0731 | grad 3.9215 | lr 0.0000 | time_forward 2.2550 | time_backward 3.0270 |
[2023-10-23 02:55:30,981::train::INFO] [train] Iter 565317 | loss 0.3032 | loss(rot) 0.1142 | loss(pos) 0.0222 | loss(seq) 0.1669 | grad 2.5160 | lr 0.0000 | time_forward 2.9600 | time_backward 4.1900 |
[2023-10-23 02:55:38,852::train::INFO] [train] Iter 565318 | loss 0.7007 | loss(rot) 0.1913 | loss(pos) 0.4983 | loss(seq) 0.0112 | grad 6.4796 | lr 0.0000 | time_forward 3.2720 | time_backward 4.5960 |
[2023-10-23 02:55:45,388::train::INFO] [train] Iter 565319 | loss 1.5940 | loss(rot) 1.4463 | loss(pos) 0.1066 | loss(seq) 0.0411 | grad 4.8715 | lr 0.0000 | time_forward 2.7850 | time_backward 3.7470 |
[2023-10-23 02:55:52,630::train::INFO] [train] Iter 565320 | loss 2.4022 | loss(rot) 2.0918 | loss(pos) 0.1033 | loss(seq) 0.2071 | grad 5.2347 | lr 0.0000 | time_forward 3.0380 | time_backward 4.2010 |
[2023-10-23 02:55:55,233::train::INFO] [train] Iter 565321 | loss 1.0843 | loss(rot) 0.4911 | loss(pos) 0.1326 | loss(seq) 0.4606 | grad 3.1742 | lr 0.0000 | time_forward 1.2440 | time_backward 1.3550 |
[2023-10-23 02:56:02,559::train::INFO] [train] Iter 565322 | loss 0.2741 | loss(rot) 0.0445 | loss(pos) 0.0457 | loss(seq) 0.1839 | grad 2.8850 | lr 0.0000 | time_forward 3.2020 | time_backward 4.1030 |
[2023-10-23 02:56:10,436::train::INFO] [train] Iter 565323 | loss 0.8822 | loss(rot) 0.5689 | loss(pos) 0.2641 | loss(seq) 0.0492 | grad 4.4660 | lr 0.0000 | time_forward 3.2690 | time_backward 4.6050 |
[2023-10-23 02:56:13,102::train::INFO] [train] Iter 565324 | loss 0.5269 | loss(rot) 0.0271 | loss(pos) 0.1240 | loss(seq) 0.3757 | grad 3.8249 | lr 0.0000 | time_forward 1.2730 | time_backward 1.3900 |
[2023-10-23 02:56:20,885::train::INFO] [train] Iter 565325 | loss 1.7256 | loss(rot) 1.6861 | loss(pos) 0.0381 | loss(seq) 0.0013 | grad 20.0353 | lr 0.0000 | time_forward 3.2270 | time_backward 4.5340 |
[2023-10-23 02:56:27,855::train::INFO] [train] Iter 565326 | loss 0.3283 | loss(rot) 0.3058 | loss(pos) 0.0223 | loss(seq) 0.0002 | grad 2.6335 | lr 0.0000 | time_forward 3.0950 | time_backward 3.8720 |
[2023-10-23 02:56:35,563::train::INFO] [train] Iter 565327 | loss 1.0032 | loss(rot) 0.5335 | loss(pos) 0.1250 | loss(seq) 0.3448 | grad 6.7875 | lr 0.0000 | time_forward 3.2010 | time_backward 4.5030 |
[2023-10-23 02:56:42,458::train::INFO] [train] Iter 565328 | loss 0.4335 | loss(rot) 0.2804 | loss(pos) 0.0260 | loss(seq) 0.1271 | grad 3.1801 | lr 0.0000 | time_forward 2.9460 | time_backward 3.9450 |
[2023-10-23 02:56:50,311::train::INFO] [train] Iter 565329 | loss 0.7301 | loss(rot) 0.3302 | loss(pos) 0.2608 | loss(seq) 0.1392 | grad 5.5682 | lr 0.0000 | time_forward 3.2220 | time_backward 4.6280 |
[2023-10-23 02:56:56,704::train::INFO] [train] Iter 565330 | loss 0.2465 | loss(rot) 0.0835 | loss(pos) 0.1393 | loss(seq) 0.0238 | grad 6.2748 | lr 0.0000 | time_forward 2.7560 | time_backward 3.6350 |
[2023-10-23 02:57:03,184::train::INFO] [train] Iter 565331 | loss 1.9657 | loss(rot) 1.4575 | loss(pos) 0.1799 | loss(seq) 0.3283 | grad 5.7310 | lr 0.0000 | time_forward 2.7600 | time_backward 3.7170 |
[2023-10-23 02:57:09,833::train::INFO] [train] Iter 565332 | loss 0.6364 | loss(rot) 0.4186 | loss(pos) 0.0112 | loss(seq) 0.2065 | grad 4.7121 | lr 0.0000 | time_forward 2.8450 | time_backward 3.8010 |
[2023-10-23 02:57:17,685::train::INFO] [train] Iter 565333 | loss 0.3096 | loss(rot) 0.0916 | loss(pos) 0.0477 | loss(seq) 0.1703 | grad 2.8192 | lr 0.0000 | time_forward 3.2560 | time_backward 4.5920 |
[2023-10-23 02:57:20,397::train::INFO] [train] Iter 565334 | loss 0.1260 | loss(rot) 0.1008 | loss(pos) 0.0251 | loss(seq) 0.0000 | grad 2.6588 | lr 0.0000 | time_forward 1.2420 | time_backward 1.4670 |
[2023-10-23 02:57:28,651::train::INFO] [train] Iter 565335 | loss 0.2617 | loss(rot) 0.1317 | loss(pos) 0.1017 | loss(seq) 0.0283 | grad 2.2238 | lr 0.0000 | time_forward 3.5030 | time_backward 4.7480 |
[2023-10-23 02:57:35,849::train::INFO] [train] Iter 565336 | loss 0.5129 | loss(rot) 0.3704 | loss(pos) 0.0282 | loss(seq) 0.1143 | grad 3.1462 | lr 0.0000 | time_forward 3.1540 | time_backward 4.0400 |
[2023-10-23 02:57:43,694::train::INFO] [train] Iter 565337 | loss 0.6800 | loss(rot) 0.2449 | loss(pos) 0.2363 | loss(seq) 0.1987 | grad 4.2459 | lr 0.0000 | time_forward 3.2630 | time_backward 4.5790 |
[2023-10-23 02:57:50,568::train::INFO] [train] Iter 565338 | loss 0.3516 | loss(rot) 0.3305 | loss(pos) 0.0205 | loss(seq) 0.0005 | grad 4.0017 | lr 0.0000 | time_forward 3.0630 | time_backward 3.8080 |
[2023-10-23 02:57:57,870::train::INFO] [train] Iter 565339 | loss 0.6209 | loss(rot) 0.1310 | loss(pos) 0.2138 | loss(seq) 0.2761 | grad 5.8646 | lr 0.0000 | time_forward 3.2010 | time_backward 4.0990 |
[2023-10-23 02:58:05,237::train::INFO] [train] Iter 565340 | loss 1.2882 | loss(rot) 0.6031 | loss(pos) 0.3001 | loss(seq) 0.3850 | grad 3.9559 | lr 0.0000 | time_forward 3.1510 | time_backward 4.2120 |
[2023-10-23 02:58:12,308::train::INFO] [train] Iter 565341 | loss 0.1252 | loss(rot) 0.0704 | loss(pos) 0.0267 | loss(seq) 0.0281 | grad 1.4540 | lr 0.0000 | time_forward 3.0930 | time_backward 3.9750 |
[2023-10-23 02:58:14,941::train::INFO] [train] Iter 565342 | loss 0.7888 | loss(rot) 0.7038 | loss(pos) 0.0458 | loss(seq) 0.0392 | grad 5.7670 | lr 0.0000 | time_forward 1.2330 | time_backward 1.3960 |
[2023-10-23 02:58:20,255::train::INFO] [train] Iter 565343 | loss 0.2757 | loss(rot) 0.0721 | loss(pos) 0.0222 | loss(seq) 0.1814 | grad 1.9211 | lr 0.0000 | time_forward 2.2790 | time_backward 3.0330 |
[2023-10-23 02:58:26,737::train::INFO] [train] Iter 565344 | loss 1.5450 | loss(rot) 1.0966 | loss(pos) 0.2339 | loss(seq) 0.2146 | grad 5.9040 | lr 0.0000 | time_forward 2.7820 | time_backward 3.6860 |
[2023-10-23 02:58:32,526::train::INFO] [train] Iter 565345 | loss 0.1563 | loss(rot) 0.1295 | loss(pos) 0.0264 | loss(seq) 0.0003 | grad 2.1715 | lr 0.0000 | time_forward 2.5070 | time_backward 3.2790 |
[2023-10-23 02:58:39,675::train::INFO] [train] Iter 565346 | loss 0.7394 | loss(rot) 0.1273 | loss(pos) 0.1506 | loss(seq) 0.4615 | grad 3.3125 | lr 0.0000 | time_forward 3.0950 | time_backward 4.0490 |
[2023-10-23 02:58:46,361::train::INFO] [train] Iter 565347 | loss 0.3291 | loss(rot) 0.0934 | loss(pos) 0.0501 | loss(seq) 0.1857 | grad 2.7892 | lr 0.0000 | time_forward 2.8800 | time_backward 3.8040 |
[2023-10-23 02:58:53,578::train::INFO] [train] Iter 565348 | loss 0.5859 | loss(rot) 0.3966 | loss(pos) 0.0305 | loss(seq) 0.1588 | grad 21.8583 | lr 0.0000 | time_forward 3.2230 | time_backward 3.9910 |
[2023-10-23 02:59:00,200::train::INFO] [train] Iter 565349 | loss 0.3904 | loss(rot) 0.1478 | loss(pos) 0.0455 | loss(seq) 0.1970 | grad 3.6382 | lr 0.0000 | time_forward 2.8140 | time_backward 3.8040 |
[2023-10-23 02:59:02,641::train::INFO] [train] Iter 565350 | loss 1.0476 | loss(rot) 0.9645 | loss(pos) 0.0357 | loss(seq) 0.0474 | grad 28.6423 | lr 0.0000 | time_forward 1.1750 | time_backward 1.2630 |
[2023-10-23 02:59:08,192::train::INFO] [train] Iter 565351 | loss 1.1344 | loss(rot) 0.5306 | loss(pos) 0.1898 | loss(seq) 0.4139 | grad 4.5912 | lr 0.0000 | time_forward 2.4440 | time_backward 3.0910 |
[2023-10-23 02:59:14,275::train::INFO] [train] Iter 565352 | loss 0.3427 | loss(rot) 0.2575 | loss(pos) 0.0370 | loss(seq) 0.0481 | grad 3.6174 | lr 0.0000 | time_forward 2.6850 | time_backward 3.3940 |
[2023-10-23 02:59:21,033::train::INFO] [train] Iter 565353 | loss 0.7787 | loss(rot) 0.7218 | loss(pos) 0.0555 | loss(seq) 0.0014 | grad 27.7280 | lr 0.0000 | time_forward 2.7930 | time_backward 3.9620 |
[2023-10-23 02:59:26,621::train::INFO] [train] Iter 565354 | loss 0.6503 | loss(rot) 0.1978 | loss(pos) 0.0333 | loss(seq) 0.4192 | grad 3.0203 | lr 0.0000 | time_forward 2.4400 | time_backward 3.1450 |
[2023-10-23 02:59:29,281::train::INFO] [train] Iter 565355 | loss 1.3522 | loss(rot) 0.9873 | loss(pos) 0.0489 | loss(seq) 0.3160 | grad 3.7392 | lr 0.0000 | time_forward 1.2610 | time_backward 1.3960 |
[2023-10-23 02:59:36,502::train::INFO] [train] Iter 565356 | loss 0.7696 | loss(rot) 0.6963 | loss(pos) 0.0363 | loss(seq) 0.0370 | grad 3.4218 | lr 0.0000 | time_forward 3.1400 | time_backward 4.0780 |
[2023-10-23 02:59:41,678::train::INFO] [train] Iter 565357 | loss 0.6642 | loss(rot) 0.4141 | loss(pos) 0.0220 | loss(seq) 0.2281 | grad 5.8951 | lr 0.0000 | time_forward 2.2880 | time_backward 2.8850 |
[2023-10-23 02:59:44,337::train::INFO] [train] Iter 565358 | loss 0.6004 | loss(rot) 0.5703 | loss(pos) 0.0302 | loss(seq) 0.0000 | grad 8.6782 | lr 0.0000 | time_forward 1.2620 | time_backward 1.3850 |
[2023-10-23 02:59:51,118::train::INFO] [train] Iter 565359 | loss 0.6438 | loss(rot) 0.0619 | loss(pos) 0.5808 | loss(seq) 0.0011 | grad 6.2189 | lr 0.0000 | time_forward 2.9590 | time_backward 3.8190 |
[2023-10-23 02:59:59,181::train::INFO] [train] Iter 565360 | loss 0.6579 | loss(rot) 0.0272 | loss(pos) 0.6268 | loss(seq) 0.0040 | grad 7.3543 | lr 0.0000 | time_forward 3.3190 | time_backward 4.7400 |
[2023-10-23 03:00:06,553::train::INFO] [train] Iter 565361 | loss 0.3500 | loss(rot) 0.3007 | loss(pos) 0.0353 | loss(seq) 0.0140 | grad 2.8005 | lr 0.0000 | time_forward 3.3090 | time_backward 4.0590 |
[2023-10-23 03:00:13,219::train::INFO] [train] Iter 565362 | loss 0.4114 | loss(rot) 0.2520 | loss(pos) 0.0197 | loss(seq) 0.1396 | grad 3.5424 | lr 0.0000 | time_forward 2.8660 | time_backward 3.7970 |
[2023-10-23 03:00:21,253::train::INFO] [train] Iter 565363 | loss 0.5404 | loss(rot) 0.1272 | loss(pos) 0.0876 | loss(seq) 0.3255 | grad 2.8618 | lr 0.0000 | time_forward 3.2740 | time_backward 4.7560 |
[2023-10-23 03:00:28,333::train::INFO] [train] Iter 565364 | loss 1.6832 | loss(rot) 1.5042 | loss(pos) 0.0417 | loss(seq) 0.1372 | grad 4.6534 | lr 0.0000 | time_forward 3.1120 | time_backward 3.9660 |
[2023-10-23 03:00:35,439::train::INFO] [train] Iter 565365 | loss 0.8656 | loss(rot) 0.3228 | loss(pos) 0.0859 | loss(seq) 0.4569 | grad 4.1217 | lr 0.0000 | time_forward 3.0780 | time_backward 4.0240 |
[2023-10-23 03:00:41,675::train::INFO] [train] Iter 565366 | loss 0.1141 | loss(rot) 0.0684 | loss(pos) 0.0413 | loss(seq) 0.0044 | grad 1.3159 | lr 0.0000 | time_forward 2.6870 | time_backward 3.5460 |
[2023-10-23 03:00:48,350::train::INFO] [train] Iter 565367 | loss 0.8645 | loss(rot) 0.8360 | loss(pos) 0.0284 | loss(seq) 0.0000 | grad 2.4677 | lr 0.0000 | time_forward 2.8920 | time_backward 3.7800 |
[2023-10-23 03:00:54,171::train::INFO] [train] Iter 565368 | loss 0.4047 | loss(rot) 0.0321 | loss(pos) 0.0223 | loss(seq) 0.3503 | grad 2.0013 | lr 0.0000 | time_forward 2.5530 | time_backward 3.2650 |
[2023-10-23 03:01:01,983::train::INFO] [train] Iter 565369 | loss 1.6620 | loss(rot) 1.5443 | loss(pos) 0.0313 | loss(seq) 0.0864 | grad 3.9177 | lr 0.0000 | time_forward 3.2730 | time_backward 4.5370 |
[2023-10-23 03:01:09,772::train::INFO] [train] Iter 565370 | loss 0.4538 | loss(rot) 0.1249 | loss(pos) 0.2701 | loss(seq) 0.0588 | grad 4.3934 | lr 0.0000 | time_forward 3.2120 | time_backward 4.5730 |
[2023-10-23 03:01:15,296::train::INFO] [train] Iter 565371 | loss 1.6291 | loss(rot) 1.1085 | loss(pos) 0.0972 | loss(seq) 0.4234 | grad 2.9504 | lr 0.0000 | time_forward 2.4020 | time_backward 3.1200 |
[2023-10-23 03:01:23,220::train::INFO] [train] Iter 565372 | loss 0.9360 | loss(rot) 0.9034 | loss(pos) 0.0321 | loss(seq) 0.0005 | grad 5.5476 | lr 0.0000 | time_forward 3.2300 | time_backward 4.6920 |
[2023-10-23 03:01:25,982::train::INFO] [train] Iter 565373 | loss 0.7936 | loss(rot) 0.4890 | loss(pos) 0.0417 | loss(seq) 0.2629 | grad 2.6600 | lr 0.0000 | time_forward 1.2990 | time_backward 1.4590 |
[2023-10-23 03:01:33,139::train::INFO] [train] Iter 565374 | loss 0.2985 | loss(rot) 0.0840 | loss(pos) 0.1988 | loss(seq) 0.0157 | grad 3.2033 | lr 0.0000 | time_forward 3.1570 | time_backward 3.9970 |
[2023-10-23 03:01:35,793::train::INFO] [train] Iter 565375 | loss 0.4081 | loss(rot) 0.2551 | loss(pos) 0.0196 | loss(seq) 0.1333 | grad 3.3792 | lr 0.0000 | time_forward 1.2820 | time_backward 1.3690 |
[2023-10-23 03:01:42,478::train::INFO] [train] Iter 565376 | loss 0.4704 | loss(rot) 0.1759 | loss(pos) 0.0206 | loss(seq) 0.2739 | grad 2.6066 | lr 0.0000 | time_forward 2.9050 | time_backward 3.7550 |
[2023-10-23 03:01:45,173::train::INFO] [train] Iter 565377 | loss 0.2510 | loss(rot) 0.1954 | loss(pos) 0.0499 | loss(seq) 0.0057 | grad 3.3577 | lr 0.0000 | time_forward 1.3250 | time_backward 1.3670 |
[2023-10-23 03:01:47,404::train::INFO] [train] Iter 565378 | loss 0.4782 | loss(rot) 0.4078 | loss(pos) 0.0147 | loss(seq) 0.0557 | grad 17.1191 | lr 0.0000 | time_forward 1.0410 | time_backward 1.1870 |
[2023-10-23 03:01:54,735::train::INFO] [train] Iter 565379 | loss 0.8603 | loss(rot) 0.3215 | loss(pos) 0.1226 | loss(seq) 0.4162 | grad 2.4412 | lr 0.0000 | time_forward 3.1580 | time_backward 4.1690 |
[2023-10-23 03:02:01,592::train::INFO] [train] Iter 565380 | loss 0.6958 | loss(rot) 0.2963 | loss(pos) 0.0912 | loss(seq) 0.3082 | grad 4.1111 | lr 0.0000 | time_forward 2.9910 | time_backward 3.8630 |
[2023-10-23 03:02:09,477::train::INFO] [train] Iter 565381 | loss 1.1804 | loss(rot) 0.7847 | loss(pos) 0.0931 | loss(seq) 0.3026 | grad 15.4236 | lr 0.0000 | time_forward 3.4430 | time_backward 4.4390 |
[2023-10-23 03:02:15,987::train::INFO] [train] Iter 565382 | loss 0.3721 | loss(rot) 0.1676 | loss(pos) 0.1158 | loss(seq) 0.0887 | grad 2.8931 | lr 0.0000 | time_forward 2.7550 | time_backward 3.7510 |
[2023-10-23 03:02:23,367::train::INFO] [train] Iter 565383 | loss 0.5278 | loss(rot) 0.0926 | loss(pos) 0.4319 | loss(seq) 0.0033 | grad 7.9276 | lr 0.0000 | time_forward 3.2250 | time_backward 4.1530 |
[2023-10-23 03:02:29,262::train::INFO] [train] Iter 565384 | loss 0.9196 | loss(rot) 0.2959 | loss(pos) 0.6209 | loss(seq) 0.0028 | grad 9.8126 | lr 0.0000 | time_forward 2.5970 | time_backward 3.2950 |
[2023-10-23 03:02:35,898::train::INFO] [train] Iter 565385 | loss 0.2485 | loss(rot) 0.2215 | loss(pos) 0.0220 | loss(seq) 0.0050 | grad 17.2862 | lr 0.0000 | time_forward 2.8020 | time_backward 3.8320 |
[2023-10-23 03:02:42,714::train::INFO] [train] Iter 565386 | loss 0.8642 | loss(rot) 0.3170 | loss(pos) 0.5457 | loss(seq) 0.0016 | grad 5.8151 | lr 0.0000 | time_forward 2.9980 | time_backward 3.8140 |
[2023-10-23 03:02:49,471::train::INFO] [train] Iter 565387 | loss 0.7607 | loss(rot) 0.6999 | loss(pos) 0.0332 | loss(seq) 0.0277 | grad 6.3414 | lr 0.0000 | time_forward 2.9360 | time_backward 3.8180 |
[2023-10-23 03:02:57,707::train::INFO] [train] Iter 565388 | loss 0.4532 | loss(rot) 0.4214 | loss(pos) 0.0255 | loss(seq) 0.0063 | grad 22.9227 | lr 0.0000 | time_forward 3.2640 | time_backward 4.9700 |
[2023-10-23 03:03:05,531::train::INFO] [train] Iter 565389 | loss 0.4247 | loss(rot) 0.1621 | loss(pos) 0.0289 | loss(seq) 0.2338 | grad 2.3066 | lr 0.0000 | time_forward 3.2590 | time_backward 4.5610 |
[2023-10-23 03:03:13,308::train::INFO] [train] Iter 565390 | loss 0.3384 | loss(rot) 0.1671 | loss(pos) 0.1072 | loss(seq) 0.0641 | grad 3.2904 | lr 0.0000 | time_forward 3.2260 | time_backward 4.5490 |
[2023-10-23 03:03:15,870::train::INFO] [train] Iter 565391 | loss 0.7443 | loss(rot) 0.5086 | loss(pos) 0.0321 | loss(seq) 0.2036 | grad 3.7243 | lr 0.0000 | time_forward 1.1660 | time_backward 1.3920 |
[2023-10-23 03:03:23,856::train::INFO] [train] Iter 565392 | loss 0.9137 | loss(rot) 0.7398 | loss(pos) 0.0157 | loss(seq) 0.1583 | grad 8.7025 | lr 0.0000 | time_forward 3.3170 | time_backward 4.6520 |
[2023-10-23 03:03:26,401::train::INFO] [train] Iter 565393 | loss 0.4276 | loss(rot) 0.0578 | loss(pos) 0.1766 | loss(seq) 0.1932 | grad 6.0835 | lr 0.0000 | time_forward 1.1860 | time_backward 1.3550 |
[2023-10-23 03:03:34,395::train::INFO] [train] Iter 565394 | loss 0.5720 | loss(rot) 0.1311 | loss(pos) 0.3621 | loss(seq) 0.0788 | grad 4.7691 | lr 0.0000 | time_forward 3.3070 | time_backward 4.6840 |
[2023-10-23 03:03:42,346::train::INFO] [train] Iter 565395 | loss 0.2777 | loss(rot) 0.0832 | loss(pos) 0.0889 | loss(seq) 0.1056 | grad 2.4885 | lr 0.0000 | time_forward 3.2940 | time_backward 4.6550 |
[2023-10-23 03:03:50,207::train::INFO] [train] Iter 565396 | loss 0.4398 | loss(rot) 0.3944 | loss(pos) 0.0249 | loss(seq) 0.0205 | grad 2.1737 | lr 0.0000 | time_forward 3.2910 | time_backward 4.5670 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.