text stringlengths 56 1.16k |
|---|
[2023-10-23 16:49:22,879::train::INFO] [train] Iter 573189 | loss 0.3409 | loss(rot) 0.1853 | loss(pos) 0.0637 | loss(seq) 0.0918 | grad 3.4424 | lr 0.0000 | time_forward 1.3470 | time_backward 1.6100 |
[2023-10-23 16:49:32,165::train::INFO] [train] Iter 573190 | loss 1.3439 | loss(rot) 0.9715 | loss(pos) 0.0329 | loss(seq) 0.3395 | grad 4.3759 | lr 0.0000 | time_forward 3.9210 | time_backward 5.3630 |
[2023-10-23 16:49:34,421::train::INFO] [train] Iter 573191 | loss 1.4973 | loss(rot) 0.8599 | loss(pos) 0.1308 | loss(seq) 0.5066 | grad 16.6431 | lr 0.0000 | time_forward 1.0360 | time_backward 1.2160 |
[2023-10-23 16:49:44,085::train::INFO] [train] Iter 573192 | loss 0.2801 | loss(rot) 0.1428 | loss(pos) 0.0877 | loss(seq) 0.0495 | grad 2.5085 | lr 0.0000 | time_forward 4.3350 | time_backward 5.3260 |
[2023-10-23 16:49:53,709::train::INFO] [train] Iter 573193 | loss 0.9582 | loss(rot) 0.7938 | loss(pos) 0.0847 | loss(seq) 0.0798 | grad 4.1376 | lr 0.0000 | time_forward 4.2980 | time_backward 5.3230 |
[2023-10-23 16:49:56,317::train::INFO] [train] Iter 573194 | loss 0.7237 | loss(rot) 0.1679 | loss(pos) 0.0412 | loss(seq) 0.5146 | grad 3.1610 | lr 0.0000 | time_forward 1.2360 | time_backward 1.3700 |
[2023-10-23 16:50:05,337::train::INFO] [train] Iter 573195 | loss 2.8815 | loss(rot) 2.1951 | loss(pos) 0.2388 | loss(seq) 0.4476 | grad 7.7904 | lr 0.0000 | time_forward 3.6480 | time_backward 5.3570 |
[2023-10-23 16:50:14,419::train::INFO] [train] Iter 573196 | loss 2.5593 | loss(rot) 2.3523 | loss(pos) 0.0946 | loss(seq) 0.1124 | grad 3.7032 | lr 0.0000 | time_forward 3.9500 | time_backward 5.1280 |
[2023-10-23 16:50:17,836::train::INFO] [train] Iter 573197 | loss 0.7456 | loss(rot) 0.0192 | loss(pos) 0.7251 | loss(seq) 0.0013 | grad 5.3765 | lr 0.0000 | time_forward 1.6030 | time_backward 1.8110 |
[2023-10-23 16:50:20,194::train::INFO] [train] Iter 573198 | loss 0.3820 | loss(rot) 0.3044 | loss(pos) 0.0208 | loss(seq) 0.0568 | grad 1.6391 | lr 0.0000 | time_forward 1.0840 | time_backward 1.2580 |
[2023-10-23 16:50:22,979::train::INFO] [train] Iter 573199 | loss 1.2813 | loss(rot) 0.0050 | loss(pos) 1.2760 | loss(seq) 0.0003 | grad 23.2442 | lr 0.0000 | time_forward 1.3520 | time_backward 1.4290 |
[2023-10-23 16:50:30,731::train::INFO] [train] Iter 573200 | loss 2.1273 | loss(rot) 1.9511 | loss(pos) 0.0400 | loss(seq) 0.1362 | grad 4.7399 | lr 0.0000 | time_forward 3.3480 | time_backward 4.3800 |
[2023-10-23 16:50:37,374::train::INFO] [train] Iter 573201 | loss 2.4821 | loss(rot) 1.6654 | loss(pos) 0.1877 | loss(seq) 0.6291 | grad 5.5746 | lr 0.0000 | time_forward 3.0080 | time_backward 3.6320 |
[2023-10-23 16:50:45,051::train::INFO] [train] Iter 573202 | loss 0.6894 | loss(rot) 0.6100 | loss(pos) 0.0244 | loss(seq) 0.0550 | grad 2.6801 | lr 0.0000 | time_forward 3.2680 | time_backward 4.3920 |
[2023-10-23 16:50:53,440::train::INFO] [train] Iter 573203 | loss 0.2975 | loss(rot) 0.0858 | loss(pos) 0.0190 | loss(seq) 0.1927 | grad 2.1153 | lr 0.0000 | time_forward 3.8990 | time_backward 4.4860 |
[2023-10-23 16:51:02,423::train::INFO] [train] Iter 573204 | loss 0.2990 | loss(rot) 0.1038 | loss(pos) 0.0376 | loss(seq) 0.1576 | grad 2.4487 | lr 0.0000 | time_forward 4.0980 | time_backward 4.8830 |
[2023-10-23 16:51:10,651::train::INFO] [train] Iter 573205 | loss 0.7965 | loss(rot) 0.6703 | loss(pos) 0.0151 | loss(seq) 0.1111 | grad 3.7382 | lr 0.0000 | time_forward 3.7390 | time_backward 4.4850 |
[2023-10-23 16:51:18,538::train::INFO] [train] Iter 573206 | loss 0.3157 | loss(rot) 0.1191 | loss(pos) 0.0405 | loss(seq) 0.1561 | grad 2.0984 | lr 0.0000 | time_forward 3.3750 | time_backward 4.5090 |
[2023-10-23 16:51:26,459::train::INFO] [train] Iter 573207 | loss 0.2789 | loss(rot) 0.2483 | loss(pos) 0.0305 | loss(seq) 0.0001 | grad 3.2955 | lr 0.0000 | time_forward 3.3770 | time_backward 4.5400 |
[2023-10-23 16:51:28,966::train::INFO] [train] Iter 573208 | loss 0.5144 | loss(rot) 0.1745 | loss(pos) 0.0325 | loss(seq) 0.3074 | grad 3.2519 | lr 0.0000 | time_forward 1.2150 | time_backward 1.2890 |
[2023-10-23 16:51:37,562::train::INFO] [train] Iter 573209 | loss 0.1011 | loss(rot) 0.0687 | loss(pos) 0.0141 | loss(seq) 0.0183 | grad 1.5804 | lr 0.0000 | time_forward 3.8730 | time_backward 4.7190 |
[2023-10-23 16:51:45,347::train::INFO] [train] Iter 573210 | loss 0.9704 | loss(rot) 0.9391 | loss(pos) 0.0313 | loss(seq) 0.0000 | grad 4.3711 | lr 0.0000 | time_forward 3.2490 | time_backward 4.5340 |
[2023-10-23 16:51:53,942::train::INFO] [train] Iter 573211 | loss 0.3654 | loss(rot) 0.1616 | loss(pos) 0.1498 | loss(seq) 0.0541 | grad 3.0948 | lr 0.0000 | time_forward 3.7260 | time_backward 4.8650 |
[2023-10-23 16:52:02,530::train::INFO] [train] Iter 573212 | loss 0.5851 | loss(rot) 0.2930 | loss(pos) 0.0508 | loss(seq) 0.2413 | grad 3.1860 | lr 0.0000 | time_forward 3.9010 | time_backward 4.6830 |
[2023-10-23 16:52:12,353::train::INFO] [train] Iter 573213 | loss 0.8672 | loss(rot) 0.5283 | loss(pos) 0.0663 | loss(seq) 0.2726 | grad 3.1806 | lr 0.0000 | time_forward 4.3890 | time_backward 5.4320 |
[2023-10-23 16:52:19,719::train::INFO] [train] Iter 573214 | loss 4.4755 | loss(rot) 0.1269 | loss(pos) 4.3486 | loss(seq) 0.0000 | grad 24.0190 | lr 0.0000 | time_forward 3.1350 | time_backward 4.2270 |
[2023-10-23 16:52:27,895::train::INFO] [train] Iter 573215 | loss 2.0749 | loss(rot) 1.9297 | loss(pos) 0.1288 | loss(seq) 0.0164 | grad 4.5374 | lr 0.0000 | time_forward 3.4590 | time_backward 4.7140 |
[2023-10-23 16:52:36,976::train::INFO] [train] Iter 573216 | loss 0.2256 | loss(rot) 0.2082 | loss(pos) 0.0172 | loss(seq) 0.0003 | grad 5.4464 | lr 0.0000 | time_forward 3.7720 | time_backward 5.3040 |
[2023-10-23 16:52:44,587::train::INFO] [train] Iter 573217 | loss 0.4922 | loss(rot) 0.4704 | loss(pos) 0.0216 | loss(seq) 0.0003 | grad 43.3357 | lr 0.0000 | time_forward 3.1910 | time_backward 4.4170 |
[2023-10-23 16:52:53,156::train::INFO] [train] Iter 573218 | loss 1.7954 | loss(rot) 0.0029 | loss(pos) 1.7925 | loss(seq) 0.0000 | grad 19.9074 | lr 0.0000 | time_forward 3.9610 | time_backward 4.6050 |
[2023-10-23 16:53:02,328::train::INFO] [train] Iter 573219 | loss 1.0591 | loss(rot) 0.5575 | loss(pos) 0.0823 | loss(seq) 0.4194 | grad 3.7056 | lr 0.0000 | time_forward 3.7950 | time_backward 5.3740 |
[2023-10-23 16:53:10,985::train::INFO] [train] Iter 573220 | loss 1.0360 | loss(rot) 0.6614 | loss(pos) 0.1513 | loss(seq) 0.2233 | grad 3.8856 | lr 0.0000 | time_forward 3.9400 | time_backward 4.7140 |
[2023-10-23 16:53:20,613::train::INFO] [train] Iter 573221 | loss 1.3790 | loss(rot) 0.8228 | loss(pos) 0.1697 | loss(seq) 0.3865 | grad 5.5914 | lr 0.0000 | time_forward 4.4080 | time_backward 5.2160 |
[2023-10-23 16:53:23,383::train::INFO] [train] Iter 573222 | loss 0.3324 | loss(rot) 0.1136 | loss(pos) 0.2098 | loss(seq) 0.0089 | grad 4.3562 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4330 |
[2023-10-23 16:53:32,672::train::INFO] [train] Iter 573223 | loss 0.3355 | loss(rot) 0.1803 | loss(pos) 0.0731 | loss(seq) 0.0821 | grad 3.6419 | lr 0.0000 | time_forward 3.7500 | time_backward 5.5360 |
[2023-10-23 16:53:42,883::train::INFO] [train] Iter 573224 | loss 0.6784 | loss(rot) 0.2176 | loss(pos) 0.2652 | loss(seq) 0.1956 | grad 6.0017 | lr 0.0000 | time_forward 4.3890 | time_backward 5.8190 |
[2023-10-23 16:53:51,405::train::INFO] [train] Iter 573225 | loss 0.4448 | loss(rot) 0.2716 | loss(pos) 0.0281 | loss(seq) 0.1451 | grad 3.4608 | lr 0.0000 | time_forward 3.6620 | time_backward 4.8570 |
[2023-10-23 16:53:59,183::train::INFO] [train] Iter 573226 | loss 0.4383 | loss(rot) 0.2808 | loss(pos) 0.0283 | loss(seq) 0.1292 | grad 4.3933 | lr 0.0000 | time_forward 3.2640 | time_backward 4.5090 |
[2023-10-23 16:54:02,096::train::INFO] [train] Iter 573227 | loss 0.6696 | loss(rot) 0.1716 | loss(pos) 0.2027 | loss(seq) 0.2952 | grad 6.1186 | lr 0.0000 | time_forward 1.3780 | time_backward 1.5330 |
[2023-10-23 16:54:11,096::train::INFO] [train] Iter 573228 | loss 1.3310 | loss(rot) 0.2672 | loss(pos) 0.5678 | loss(seq) 0.4959 | grad 3.3027 | lr 0.0000 | time_forward 4.0980 | time_backward 4.8990 |
[2023-10-23 16:54:19,130::train::INFO] [train] Iter 573229 | loss 2.6661 | loss(rot) 2.6153 | loss(pos) 0.0508 | loss(seq) 0.0000 | grad 4.5253 | lr 0.0000 | time_forward 3.4290 | time_backward 4.6010 |
[2023-10-23 16:54:29,209::train::INFO] [train] Iter 573230 | loss 1.1063 | loss(rot) 0.9074 | loss(pos) 0.0488 | loss(seq) 0.1500 | grad 7.6688 | lr 0.0000 | time_forward 4.2470 | time_backward 5.8280 |
[2023-10-23 16:54:36,661::train::INFO] [train] Iter 573231 | loss 0.2428 | loss(rot) 0.1368 | loss(pos) 0.0878 | loss(seq) 0.0183 | grad 3.0199 | lr 0.0000 | time_forward 3.3880 | time_backward 4.0610 |
[2023-10-23 16:54:46,086::train::INFO] [train] Iter 573232 | loss 3.3101 | loss(rot) 0.0027 | loss(pos) 3.3074 | loss(seq) 0.0000 | grad 17.9575 | lr 0.0000 | time_forward 4.3370 | time_backward 5.0850 |
[2023-10-23 16:54:54,563::train::INFO] [train] Iter 573233 | loss 0.3068 | loss(rot) 0.0552 | loss(pos) 0.2280 | loss(seq) 0.0236 | grad 8.4684 | lr 0.0000 | time_forward 3.5960 | time_backward 4.8780 |
[2023-10-23 16:55:03,564::train::INFO] [train] Iter 573234 | loss 0.4386 | loss(rot) 0.3728 | loss(pos) 0.0658 | loss(seq) 0.0000 | grad 11.5790 | lr 0.0000 | time_forward 4.1210 | time_backward 4.8770 |
[2023-10-23 16:55:12,765::train::INFO] [train] Iter 573235 | loss 0.6091 | loss(rot) 0.0116 | loss(pos) 0.5946 | loss(seq) 0.0029 | grad 6.5525 | lr 0.0000 | time_forward 3.7840 | time_backward 5.4140 |
[2023-10-23 16:55:21,006::train::INFO] [train] Iter 573236 | loss 0.2531 | loss(rot) 0.0496 | loss(pos) 0.1270 | loss(seq) 0.0765 | grad 4.3421 | lr 0.0000 | time_forward 3.7290 | time_backward 4.5090 |
[2023-10-23 16:55:23,307::train::INFO] [train] Iter 573237 | loss 0.3355 | loss(rot) 0.1605 | loss(pos) 0.1203 | loss(seq) 0.0547 | grad 3.4187 | lr 0.0000 | time_forward 1.0610 | time_backward 1.2360 |
[2023-10-23 16:55:31,382::train::INFO] [train] Iter 573238 | loss 1.1854 | loss(rot) 0.6965 | loss(pos) 0.0454 | loss(seq) 0.4434 | grad 4.2490 | lr 0.0000 | time_forward 3.4480 | time_backward 4.6000 |
[2023-10-23 16:55:41,166::train::INFO] [train] Iter 573239 | loss 0.5802 | loss(rot) 0.2797 | loss(pos) 0.0226 | loss(seq) 0.2779 | grad 2.5106 | lr 0.0000 | time_forward 4.3000 | time_backward 5.4800 |
[2023-10-23 16:55:43,930::train::INFO] [train] Iter 573240 | loss 1.8396 | loss(rot) 1.1159 | loss(pos) 0.1168 | loss(seq) 0.6070 | grad 5.2678 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4530 |
[2023-10-23 16:55:53,319::train::INFO] [train] Iter 573241 | loss 0.1487 | loss(rot) 0.1173 | loss(pos) 0.0242 | loss(seq) 0.0072 | grad 2.0197 | lr 0.0000 | time_forward 4.0160 | time_backward 5.3680 |
[2023-10-23 16:56:00,871::train::INFO] [train] Iter 573242 | loss 0.7581 | loss(rot) 0.2518 | loss(pos) 0.0493 | loss(seq) 0.4570 | grad 4.4884 | lr 0.0000 | time_forward 3.2120 | time_backward 4.3380 |
[2023-10-23 16:56:12,211::train::INFO] [train] Iter 573243 | loss 0.5408 | loss(rot) 0.3224 | loss(pos) 0.0593 | loss(seq) 0.1591 | grad 3.7824 | lr 0.0000 | time_forward 4.3010 | time_backward 7.0350 |
[2023-10-23 16:56:21,244::train::INFO] [train] Iter 573244 | loss 0.1612 | loss(rot) 0.1216 | loss(pos) 0.0394 | loss(seq) 0.0002 | grad 3.0903 | lr 0.0000 | time_forward 4.1700 | time_backward 4.8590 |
[2023-10-23 16:56:24,540::train::INFO] [train] Iter 573245 | loss 0.3154 | loss(rot) 0.2498 | loss(pos) 0.0134 | loss(seq) 0.0522 | grad 3.0391 | lr 0.0000 | time_forward 1.4830 | time_backward 1.8100 |
[2023-10-23 16:56:31,173::train::INFO] [train] Iter 573246 | loss 0.4356 | loss(rot) 0.3964 | loss(pos) 0.0147 | loss(seq) 0.0245 | grad 4.7966 | lr 0.0000 | time_forward 2.8720 | time_backward 3.7460 |
[2023-10-23 16:56:37,481::train::INFO] [train] Iter 573247 | loss 0.4074 | loss(rot) 0.2606 | loss(pos) 0.0335 | loss(seq) 0.1133 | grad 19.7036 | lr 0.0000 | time_forward 2.7890 | time_backward 3.5170 |
[2023-10-23 16:56:44,726::train::INFO] [train] Iter 573248 | loss 1.4355 | loss(rot) 0.4665 | loss(pos) 0.5580 | loss(seq) 0.4110 | grad 6.3535 | lr 0.0000 | time_forward 3.0930 | time_backward 4.1320 |
[2023-10-23 16:56:52,075::train::INFO] [train] Iter 573249 | loss 1.8824 | loss(rot) 1.6798 | loss(pos) 0.0511 | loss(seq) 0.1515 | grad 6.6966 | lr 0.0000 | time_forward 3.0770 | time_backward 4.2700 |
[2023-10-23 16:57:00,091::train::INFO] [train] Iter 573250 | loss 0.4043 | loss(rot) 0.0250 | loss(pos) 0.3756 | loss(seq) 0.0037 | grad 8.3745 | lr 0.0000 | time_forward 3.6070 | time_backward 4.4040 |
[2023-10-23 16:57:08,437::train::INFO] [train] Iter 573251 | loss 0.8597 | loss(rot) 0.8285 | loss(pos) 0.0312 | loss(seq) 0.0000 | grad 13.5968 | lr 0.0000 | time_forward 3.7720 | time_backward 4.5720 |
[2023-10-23 16:57:17,633::train::INFO] [train] Iter 573252 | loss 2.1227 | loss(rot) 2.0893 | loss(pos) 0.0271 | loss(seq) 0.0063 | grad 3.8275 | lr 0.0000 | time_forward 3.8280 | time_backward 5.3650 |
[2023-10-23 16:57:26,131::train::INFO] [train] Iter 573253 | loss 0.0930 | loss(rot) 0.0655 | loss(pos) 0.0269 | loss(seq) 0.0007 | grad 1.3167 | lr 0.0000 | time_forward 3.8380 | time_backward 4.6560 |
[2023-10-23 16:57:36,113::train::INFO] [train] Iter 573254 | loss 0.1820 | loss(rot) 0.0801 | loss(pos) 0.0840 | loss(seq) 0.0178 | grad 2.1300 | lr 0.0000 | time_forward 4.2820 | time_backward 5.6970 |
[2023-10-23 16:57:38,898::train::INFO] [train] Iter 573255 | loss 1.5163 | loss(rot) 1.2498 | loss(pos) 0.0494 | loss(seq) 0.2172 | grad 6.2120 | lr 0.0000 | time_forward 1.3170 | time_backward 1.4650 |
[2023-10-23 16:57:48,497::train::INFO] [train] Iter 573256 | loss 0.5849 | loss(rot) 0.3000 | loss(pos) 0.1108 | loss(seq) 0.1741 | grad 3.1802 | lr 0.0000 | time_forward 4.2580 | time_backward 5.3370 |
[2023-10-23 16:57:51,341::train::INFO] [train] Iter 573257 | loss 0.6131 | loss(rot) 0.5314 | loss(pos) 0.0417 | loss(seq) 0.0400 | grad 2.2854 | lr 0.0000 | time_forward 1.3820 | time_backward 1.4580 |
[2023-10-23 16:58:00,972::train::INFO] [train] Iter 573258 | loss 0.6914 | loss(rot) 0.6477 | loss(pos) 0.0385 | loss(seq) 0.0051 | grad 2.8178 | lr 0.0000 | time_forward 4.4020 | time_backward 5.2260 |
[2023-10-23 16:58:03,883::train::INFO] [train] Iter 573259 | loss 0.3369 | loss(rot) 0.1803 | loss(pos) 0.0264 | loss(seq) 0.1301 | grad 3.7656 | lr 0.0000 | time_forward 1.4400 | time_backward 1.4690 |
[2023-10-23 16:58:11,570::train::INFO] [train] Iter 573260 | loss 0.5441 | loss(rot) 0.3633 | loss(pos) 0.0578 | loss(seq) 0.1230 | grad 2.8745 | lr 0.0000 | time_forward 3.3190 | time_backward 4.3650 |
[2023-10-23 16:58:14,359::train::INFO] [train] Iter 573261 | loss 0.5445 | loss(rot) 0.0438 | loss(pos) 0.0613 | loss(seq) 0.4393 | grad 3.1583 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4610 |
[2023-10-23 16:58:23,007::train::INFO] [train] Iter 573262 | loss 0.9786 | loss(rot) 0.5632 | loss(pos) 0.1571 | loss(seq) 0.2584 | grad 5.3415 | lr 0.0000 | time_forward 3.7010 | time_backward 4.9110 |
[2023-10-23 16:58:31,951::train::INFO] [train] Iter 573263 | loss 0.5934 | loss(rot) 0.4455 | loss(pos) 0.0221 | loss(seq) 0.1258 | grad 3.7065 | lr 0.0000 | time_forward 4.0820 | time_backward 4.8580 |
[2023-10-23 16:58:41,202::train::INFO] [train] Iter 573264 | loss 0.2930 | loss(rot) 0.0712 | loss(pos) 0.0512 | loss(seq) 0.1706 | grad 1.9982 | lr 0.0000 | time_forward 3.8170 | time_backward 5.4310 |
[2023-10-23 16:58:43,989::train::INFO] [train] Iter 573265 | loss 0.5128 | loss(rot) 0.1219 | loss(pos) 0.3756 | loss(seq) 0.0153 | grad 6.1165 | lr 0.0000 | time_forward 1.3520 | time_backward 1.4310 |
[2023-10-23 16:58:52,294::train::INFO] [train] Iter 573266 | loss 0.6214 | loss(rot) 0.5044 | loss(pos) 0.0232 | loss(seq) 0.0938 | grad 3.9597 | lr 0.0000 | time_forward 3.5670 | time_backward 4.7350 |
[2023-10-23 16:58:55,130::train::INFO] [train] Iter 573267 | loss 0.3354 | loss(rot) 0.0537 | loss(pos) 0.2232 | loss(seq) 0.0586 | grad 4.5033 | lr 0.0000 | time_forward 1.3950 | time_backward 1.4370 |
[2023-10-23 16:58:57,961::train::INFO] [train] Iter 573268 | loss 0.5218 | loss(rot) 0.3732 | loss(pos) 0.0353 | loss(seq) 0.1134 | grad 2.5035 | lr 0.0000 | time_forward 1.3900 | time_backward 1.4380 |
[2023-10-23 16:59:00,491::train::INFO] [train] Iter 573269 | loss 0.6224 | loss(rot) 0.5779 | loss(pos) 0.0361 | loss(seq) 0.0083 | grad 2.6380 | lr 0.0000 | time_forward 1.2560 | time_backward 1.2690 |
[2023-10-23 16:59:10,452::train::INFO] [train] Iter 573270 | loss 0.8350 | loss(rot) 0.7626 | loss(pos) 0.0449 | loss(seq) 0.0276 | grad 6.4100 | lr 0.0000 | time_forward 4.4590 | time_backward 5.4670 |
[2023-10-23 16:59:19,720::train::INFO] [train] Iter 573271 | loss 0.4952 | loss(rot) 0.1260 | loss(pos) 0.3309 | loss(seq) 0.0383 | grad 2.8873 | lr 0.0000 | time_forward 3.8510 | time_backward 5.4150 |
[2023-10-23 16:59:29,002::train::INFO] [train] Iter 573272 | loss 0.5445 | loss(rot) 0.4204 | loss(pos) 0.0339 | loss(seq) 0.0902 | grad 4.9770 | lr 0.0000 | time_forward 3.8110 | time_backward 5.4680 |
[2023-10-23 16:59:31,644::train::INFO] [train] Iter 573273 | loss 0.3981 | loss(rot) 0.3588 | loss(pos) 0.0156 | loss(seq) 0.0237 | grad 45.7180 | lr 0.0000 | time_forward 1.2120 | time_backward 1.4270 |
[2023-10-23 16:59:34,555::train::INFO] [train] Iter 573274 | loss 0.3115 | loss(rot) 0.2766 | loss(pos) 0.0350 | loss(seq) 0.0000 | grad 2.6408 | lr 0.0000 | time_forward 1.3150 | time_backward 1.5920 |
[2023-10-23 16:59:42,850::train::INFO] [train] Iter 573275 | loss 0.3258 | loss(rot) 0.1700 | loss(pos) 0.0234 | loss(seq) 0.1325 | grad 2.0420 | lr 0.0000 | time_forward 3.7840 | time_backward 4.5060 |
[2023-10-23 16:59:50,715::train::INFO] [train] Iter 573276 | loss 0.5136 | loss(rot) 0.0921 | loss(pos) 0.0557 | loss(seq) 0.3658 | grad 4.0654 | lr 0.0000 | time_forward 3.3130 | time_backward 4.5490 |
[2023-10-23 16:59:53,496::train::INFO] [train] Iter 573277 | loss 0.3570 | loss(rot) 0.3159 | loss(pos) 0.0411 | loss(seq) 0.0000 | grad 2.8960 | lr 0.0000 | time_forward 1.3630 | time_backward 1.4140 |
[2023-10-23 16:59:56,356::train::INFO] [train] Iter 573278 | loss 0.2727 | loss(rot) 0.2432 | loss(pos) 0.0197 | loss(seq) 0.0098 | grad 3.1094 | lr 0.0000 | time_forward 1.3800 | time_backward 1.4380 |
[2023-10-23 17:00:05,007::train::INFO] [train] Iter 573279 | loss 0.3711 | loss(rot) 0.1546 | loss(pos) 0.1925 | loss(seq) 0.0239 | grad 3.9958 | lr 0.0000 | time_forward 3.6570 | time_backward 4.9510 |
[2023-10-23 17:00:07,773::train::INFO] [train] Iter 573280 | loss 0.4163 | loss(rot) 0.1148 | loss(pos) 0.1210 | loss(seq) 0.1805 | grad 3.5780 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4200 |
[2023-10-23 17:00:17,047::train::INFO] [train] Iter 573281 | loss 0.6097 | loss(rot) 0.2073 | loss(pos) 0.0738 | loss(seq) 0.3286 | grad 2.6871 | lr 0.0000 | time_forward 3.8330 | time_backward 5.4050 |
[2023-10-23 17:00:24,780::train::INFO] [train] Iter 573282 | loss 1.3214 | loss(rot) 0.6863 | loss(pos) 0.5483 | loss(seq) 0.0868 | grad 6.7677 | lr 0.0000 | time_forward 3.2780 | time_backward 4.4520 |
[2023-10-23 17:00:27,057::train::INFO] [train] Iter 573283 | loss 0.1341 | loss(rot) 0.1075 | loss(pos) 0.0257 | loss(seq) 0.0009 | grad 2.2342 | lr 0.0000 | time_forward 1.0820 | time_backward 1.1920 |
[2023-10-23 17:00:35,587::train::INFO] [train] Iter 573284 | loss 0.5293 | loss(rot) 0.2188 | loss(pos) 0.2852 | loss(seq) 0.0252 | grad 5.1462 | lr 0.0000 | time_forward 3.8870 | time_backward 4.6400 |
[2023-10-23 17:00:44,537::train::INFO] [train] Iter 573285 | loss 0.4343 | loss(rot) 0.1755 | loss(pos) 0.0756 | loss(seq) 0.1832 | grad 2.6672 | lr 0.0000 | time_forward 3.7980 | time_backward 5.1490 |
[2023-10-23 17:00:54,329::train::INFO] [train] Iter 573286 | loss 0.5076 | loss(rot) 0.3540 | loss(pos) 0.0578 | loss(seq) 0.0958 | grad 2.3325 | lr 0.0000 | time_forward 4.1340 | time_backward 5.6560 |
[2023-10-23 17:01:02,460::train::INFO] [train] Iter 573287 | loss 0.7125 | loss(rot) 0.4485 | loss(pos) 0.0614 | loss(seq) 0.2025 | grad 4.7378 | lr 0.0000 | time_forward 3.5140 | time_backward 4.6130 |
[2023-10-23 17:01:11,246::train::INFO] [train] Iter 573288 | loss 0.3174 | loss(rot) 0.0892 | loss(pos) 0.0445 | loss(seq) 0.1837 | grad 2.4721 | lr 0.0000 | time_forward 3.8330 | time_backward 4.9500 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.