text
stringlengths 56
1.16k
|
|---|
[2023-09-01 20:48:34,305::train::INFO] [train] Iter 02193 | loss 1.6854 | loss(rot) 0.7568 | loss(pos) 0.6364 | loss(seq) 0.2922 | grad 8.9796 | lr 0.0010 | time_forward 3.7820 | time_backward 5.2050 |
[2023-09-01 20:48:42,757::train::INFO] [train] Iter 02194 | loss 2.7031 | loss(rot) 1.9062 | loss(pos) 0.3007 | loss(seq) 0.4961 | grad 2.9470 | lr 0.0010 | time_forward 3.5310 | time_backward 4.9180 |
[2023-09-01 20:48:52,756::train::INFO] [train] Iter 02195 | loss 3.1101 | loss(rot) 2.9030 | loss(pos) 0.2051 | loss(seq) 0.0020 | grad 1.9114 | lr 0.0010 | time_forward 4.1830 | time_backward 5.8130 |
[2023-09-01 20:49:03,064::train::INFO] [train] Iter 02196 | loss 1.8172 | loss(rot) 0.7896 | loss(pos) 0.6124 | loss(seq) 0.4151 | grad 3.2158 | lr 0.0010 | time_forward 4.2930 | time_backward 6.0110 |
[2023-09-01 20:49:11,606::train::INFO] [train] Iter 02197 | loss 1.3145 | loss(rot) 0.6471 | loss(pos) 0.4146 | loss(seq) 0.2528 | grad 4.0625 | lr 0.0010 | time_forward 3.5250 | time_backward 5.0130 |
[2023-09-01 20:49:19,690::train::INFO] [train] Iter 02198 | loss 3.3184 | loss(rot) 2.8231 | loss(pos) 0.2331 | loss(seq) 0.2623 | grad 3.3489 | lr 0.0010 | time_forward 3.3320 | time_backward 4.7480 |
[2023-09-01 20:49:22,015::train::INFO] [train] Iter 02199 | loss 3.1743 | loss(rot) 2.3406 | loss(pos) 0.3195 | loss(seq) 0.5142 | grad 4.4332 | lr 0.0010 | time_forward 1.0450 | time_backward 1.2770 |
[2023-09-01 20:49:32,150::train::INFO] [train] Iter 02200 | loss 2.1177 | loss(rot) 0.3244 | loss(pos) 1.7805 | loss(seq) 0.0127 | grad 5.3425 | lr 0.0010 | time_forward 4.0480 | time_backward 6.0830 |
[2023-09-01 20:49:34,929::train::INFO] [train] Iter 02201 | loss 3.0336 | loss(rot) 2.8404 | loss(pos) 0.1928 | loss(seq) 0.0003 | grad 2.6856 | lr 0.0010 | time_forward 1.2410 | time_backward 1.5340 |
[2023-09-01 20:49:37,802::train::INFO] [train] Iter 02202 | loss 1.8296 | loss(rot) 0.9252 | loss(pos) 0.3556 | loss(seq) 0.5489 | grad 4.0539 | lr 0.0010 | time_forward 1.2900 | time_backward 1.5560 |
[2023-09-01 20:49:47,996::train::INFO] [train] Iter 02203 | loss 3.1745 | loss(rot) 2.3340 | loss(pos) 0.4054 | loss(seq) 0.4351 | grad 3.9218 | lr 0.0010 | time_forward 4.1550 | time_backward 6.0050 |
[2023-09-01 20:49:55,975::train::INFO] [train] Iter 02204 | loss 1.8454 | loss(rot) 1.4523 | loss(pos) 0.1074 | loss(seq) 0.2858 | grad 3.3012 | lr 0.0010 | time_forward 3.3350 | time_backward 4.6410 |
[2023-09-01 20:50:04,632::train::INFO] [train] Iter 02205 | loss 2.4810 | loss(rot) 1.5327 | loss(pos) 0.3660 | loss(seq) 0.5823 | grad 4.1912 | lr 0.0010 | time_forward 3.6020 | time_backward 5.0520 |
[2023-09-01 20:50:14,807::train::INFO] [train] Iter 02206 | loss 3.3460 | loss(rot) 2.9573 | loss(pos) 0.1591 | loss(seq) 0.2296 | grad 3.2239 | lr 0.0010 | time_forward 4.1020 | time_backward 6.0690 |
[2023-09-01 20:50:23,241::train::INFO] [train] Iter 02207 | loss 3.3386 | loss(rot) 3.0372 | loss(pos) 0.0989 | loss(seq) 0.2024 | grad 5.0084 | lr 0.0010 | time_forward 3.5180 | time_backward 4.9020 |
[2023-09-01 20:50:33,463::train::INFO] [train] Iter 02208 | loss 2.4207 | loss(rot) 1.0467 | loss(pos) 0.5359 | loss(seq) 0.8381 | grad 3.0360 | lr 0.0010 | time_forward 4.0480 | time_backward 6.1720 |
[2023-09-01 20:50:43,381::train::INFO] [train] Iter 02209 | loss 2.2238 | loss(rot) 1.9247 | loss(pos) 0.1901 | loss(seq) 0.1090 | grad 3.4124 | lr 0.0010 | time_forward 4.0470 | time_backward 5.8680 |
[2023-09-01 20:50:52,541::train::INFO] [train] Iter 02210 | loss 3.4262 | loss(rot) 2.6359 | loss(pos) 0.3035 | loss(seq) 0.4868 | grad 2.9394 | lr 0.0010 | time_forward 3.8520 | time_backward 5.3030 |
[2023-09-01 20:51:01,107::train::INFO] [train] Iter 02211 | loss 2.8930 | loss(rot) 2.2676 | loss(pos) 0.2216 | loss(seq) 0.4038 | grad 3.7249 | lr 0.0010 | time_forward 3.5970 | time_backward 4.9670 |
[2023-09-01 20:51:03,761::train::INFO] [train] Iter 02212 | loss 3.5309 | loss(rot) 3.2281 | loss(pos) 0.1591 | loss(seq) 0.1437 | grad 2.8651 | lr 0.0010 | time_forward 1.2570 | time_backward 1.3940 |
[2023-09-01 20:51:06,520::train::INFO] [train] Iter 02213 | loss 2.1535 | loss(rot) 1.0017 | loss(pos) 0.7303 | loss(seq) 0.4214 | grad 4.2077 | lr 0.0010 | time_forward 1.3090 | time_backward 1.4470 |
[2023-09-01 20:51:15,004::train::INFO] [train] Iter 02214 | loss 2.8793 | loss(rot) 2.5174 | loss(pos) 0.2633 | loss(seq) 0.0986 | grad 4.1144 | lr 0.0010 | time_forward 3.5770 | time_backward 4.9040 |
[2023-09-01 20:51:25,374::train::INFO] [train] Iter 02215 | loss 3.3474 | loss(rot) 2.7954 | loss(pos) 0.2449 | loss(seq) 0.3072 | grad 2.2180 | lr 0.0010 | time_forward 4.1210 | time_backward 6.2450 |
[2023-09-01 20:51:34,148::train::INFO] [train] Iter 02216 | loss 3.4631 | loss(rot) 1.6897 | loss(pos) 0.9727 | loss(seq) 0.8008 | grad 8.2181 | lr 0.0010 | time_forward 3.6700 | time_backward 5.1000 |
[2023-09-01 20:51:36,818::train::INFO] [train] Iter 02217 | loss 3.0132 | loss(rot) 2.9036 | loss(pos) 0.1045 | loss(seq) 0.0051 | grad 2.5919 | lr 0.0010 | time_forward 1.2170 | time_backward 1.4490 |
[2023-09-01 20:51:39,563::train::INFO] [train] Iter 02218 | loss 1.4242 | loss(rot) 0.8015 | loss(pos) 0.3885 | loss(seq) 0.2343 | grad 5.2038 | lr 0.0010 | time_forward 1.2540 | time_backward 1.4870 |
[2023-09-01 20:51:41,297::train::INFO] [train] Iter 02219 | loss 3.8145 | loss(rot) 3.4060 | loss(pos) 0.4074 | loss(seq) 0.0011 | grad 3.9925 | lr 0.0010 | time_forward 0.7590 | time_backward 0.9350 |
[2023-09-01 20:51:51,291::train::INFO] [train] Iter 02220 | loss 3.7144 | loss(rot) 0.0952 | loss(pos) 3.6192 | loss(seq) 0.0000 | grad 13.9072 | lr 0.0010 | time_forward 4.0850 | time_backward 5.9060 |
[2023-09-01 20:52:01,249::train::INFO] [train] Iter 02221 | loss 3.1471 | loss(rot) 2.2668 | loss(pos) 0.3690 | loss(seq) 0.5112 | grad 2.3233 | lr 0.0010 | time_forward 4.1630 | time_backward 5.7930 |
[2023-09-01 20:52:08,855::train::INFO] [train] Iter 02222 | loss 2.1405 | loss(rot) 1.2850 | loss(pos) 0.3510 | loss(seq) 0.5046 | grad 4.1944 | lr 0.0010 | time_forward 3.2090 | time_backward 4.3930 |
[2023-09-01 20:52:16,849::train::INFO] [train] Iter 02223 | loss 2.0519 | loss(rot) 0.0954 | loss(pos) 1.9527 | loss(seq) 0.0038 | grad 6.0552 | lr 0.0010 | time_forward 3.3730 | time_backward 4.6170 |
[2023-09-01 20:52:19,545::train::INFO] [train] Iter 02224 | loss 2.3548 | loss(rot) 1.5601 | loss(pos) 0.4540 | loss(seq) 0.3408 | grad 5.2264 | lr 0.0010 | time_forward 1.2540 | time_backward 1.4380 |
[2023-09-01 20:52:29,094::train::INFO] [train] Iter 02225 | loss 3.2436 | loss(rot) 2.9489 | loss(pos) 0.2861 | loss(seq) 0.0086 | grad 3.5201 | lr 0.0010 | time_forward 3.9220 | time_backward 5.5860 |
[2023-09-01 20:52:39,372::train::INFO] [train] Iter 02226 | loss 1.6974 | loss(rot) 0.1928 | loss(pos) 1.4846 | loss(seq) 0.0200 | grad 5.6548 | lr 0.0010 | time_forward 4.2380 | time_backward 6.0360 |
[2023-09-01 20:52:48,530::train::INFO] [train] Iter 02227 | loss 2.1952 | loss(rot) 0.9575 | loss(pos) 0.6877 | loss(seq) 0.5501 | grad 3.2829 | lr 0.0010 | time_forward 3.8380 | time_backward 5.3170 |
[2023-09-01 20:52:56,841::train::INFO] [train] Iter 02228 | loss 2.9994 | loss(rot) 2.3485 | loss(pos) 0.3329 | loss(seq) 0.3180 | grad 3.9055 | lr 0.0010 | time_forward 3.4860 | time_backward 4.8230 |
[2023-09-01 20:53:05,529::train::INFO] [train] Iter 02229 | loss 2.8264 | loss(rot) 0.0689 | loss(pos) 2.7569 | loss(seq) 0.0007 | grad 5.3516 | lr 0.0010 | time_forward 3.6060 | time_backward 5.0770 |
[2023-09-01 20:53:15,006::train::INFO] [train] Iter 02230 | loss 2.6611 | loss(rot) 0.1360 | loss(pos) 2.5162 | loss(seq) 0.0089 | grad 7.5780 | lr 0.0010 | time_forward 3.9600 | time_backward 5.5130 |
[2023-09-01 20:53:23,505::train::INFO] [train] Iter 02231 | loss 3.5791 | loss(rot) 3.2219 | loss(pos) 0.3387 | loss(seq) 0.0185 | grad 5.8217 | lr 0.0010 | time_forward 3.5680 | time_backward 4.9270 |
[2023-09-01 20:53:32,644::train::INFO] [train] Iter 02232 | loss 3.2697 | loss(rot) 2.5918 | loss(pos) 0.2761 | loss(seq) 0.4018 | grad 3.5981 | lr 0.0010 | time_forward 3.8950 | time_backward 5.2410 |
[2023-09-01 20:53:35,334::train::INFO] [train] Iter 02233 | loss 1.5699 | loss(rot) 0.1764 | loss(pos) 1.3695 | loss(seq) 0.0240 | grad 6.2693 | lr 0.0010 | time_forward 1.2610 | time_backward 1.4260 |
[2023-09-01 20:53:44,510::train::INFO] [train] Iter 02234 | loss 1.5298 | loss(rot) 0.1237 | loss(pos) 1.1157 | loss(seq) 0.2904 | grad 3.7129 | lr 0.0010 | time_forward 3.9160 | time_backward 5.2560 |
[2023-09-01 20:53:52,536::train::INFO] [train] Iter 02235 | loss 1.7386 | loss(rot) 0.0841 | loss(pos) 1.6417 | loss(seq) 0.0128 | grad 5.6434 | lr 0.0010 | time_forward 3.3300 | time_backward 4.6920 |
[2023-09-01 20:53:55,288::train::INFO] [train] Iter 02236 | loss 2.9826 | loss(rot) 2.2306 | loss(pos) 0.3090 | loss(seq) 0.4430 | grad 3.6305 | lr 0.0010 | time_forward 1.2660 | time_backward 1.4820 |
[2023-09-01 20:53:57,520::train::INFO] [train] Iter 02237 | loss 3.5269 | loss(rot) 2.5393 | loss(pos) 0.4423 | loss(seq) 0.5452 | grad 4.1340 | lr 0.0010 | time_forward 1.0190 | time_backward 1.2090 |
[2023-09-01 20:54:05,859::train::INFO] [train] Iter 02238 | loss 3.4039 | loss(rot) 3.1702 | loss(pos) 0.2271 | loss(seq) 0.0066 | grad 3.3203 | lr 0.0010 | time_forward 3.5130 | time_backward 4.8230 |
[2023-09-01 20:54:15,173::train::INFO] [train] Iter 02239 | loss 2.4481 | loss(rot) 1.6910 | loss(pos) 0.3806 | loss(seq) 0.3764 | grad 3.8266 | lr 0.0010 | time_forward 3.9170 | time_backward 5.3940 |
[2023-09-01 20:54:17,505::train::INFO] [train] Iter 02240 | loss 2.5481 | loss(rot) 0.7056 | loss(pos) 0.9103 | loss(seq) 0.9322 | grad 6.5448 | lr 0.0010 | time_forward 1.1020 | time_backward 1.2270 |
[2023-09-01 20:54:25,011::train::INFO] [train] Iter 02241 | loss 3.2387 | loss(rot) 2.8546 | loss(pos) 0.3067 | loss(seq) 0.0774 | grad 4.2172 | lr 0.0010 | time_forward 3.1080 | time_backward 4.3940 |
[2023-09-01 20:54:33,411::train::INFO] [train] Iter 02242 | loss 2.1626 | loss(rot) 0.4229 | loss(pos) 1.5905 | loss(seq) 0.1492 | grad 7.7877 | lr 0.0010 | time_forward 3.4840 | time_backward 4.9130 |
[2023-09-01 20:54:43,765::train::INFO] [train] Iter 02243 | loss 1.4716 | loss(rot) 0.1197 | loss(pos) 1.3325 | loss(seq) 0.0193 | grad 4.5525 | lr 0.0010 | time_forward 4.0290 | time_backward 6.3210 |
[2023-09-01 20:54:46,801::train::INFO] [train] Iter 02244 | loss 1.6693 | loss(rot) 0.2170 | loss(pos) 1.4174 | loss(seq) 0.0349 | grad 5.2186 | lr 0.0010 | time_forward 1.4120 | time_backward 1.6210 |
[2023-09-01 20:54:54,925::train::INFO] [train] Iter 02245 | loss 3.1574 | loss(rot) 2.9095 | loss(pos) 0.1955 | loss(seq) 0.0524 | grad 3.9326 | lr 0.0010 | time_forward 3.4830 | time_backward 4.6380 |
[2023-09-01 20:55:04,777::train::INFO] [train] Iter 02246 | loss 2.8596 | loss(rot) 2.7320 | loss(pos) 0.1227 | loss(seq) 0.0049 | grad 2.7941 | lr 0.0010 | time_forward 4.0490 | time_backward 5.7980 |
[2023-09-01 20:55:14,791::train::INFO] [train] Iter 02247 | loss 3.2017 | loss(rot) 2.8958 | loss(pos) 0.2784 | loss(seq) 0.0274 | grad 3.5392 | lr 0.0010 | time_forward 4.0260 | time_backward 5.9840 |
[2023-09-01 20:55:17,486::train::INFO] [train] Iter 02248 | loss 2.5666 | loss(rot) 1.6957 | loss(pos) 0.4397 | loss(seq) 0.4312 | grad 4.7682 | lr 0.0010 | time_forward 1.2900 | time_backward 1.4020 |
[2023-09-01 20:55:20,188::train::INFO] [train] Iter 02249 | loss 3.5576 | loss(rot) 2.7407 | loss(pos) 0.2927 | loss(seq) 0.5242 | grad 2.7581 | lr 0.0010 | time_forward 1.2740 | time_backward 1.4250 |
[2023-09-01 20:55:30,023::train::INFO] [train] Iter 02250 | loss 1.3591 | loss(rot) 0.4070 | loss(pos) 0.8628 | loss(seq) 0.0892 | grad 4.7377 | lr 0.0010 | time_forward 3.9720 | time_backward 5.8170 |
[2023-09-01 20:55:32,711::train::INFO] [train] Iter 02251 | loss 2.3494 | loss(rot) 1.4684 | loss(pos) 0.3532 | loss(seq) 0.5277 | grad 3.6138 | lr 0.0010 | time_forward 1.2550 | time_backward 1.4180 |
[2023-09-01 20:55:39,127::train::INFO] [train] Iter 02252 | loss 1.3748 | loss(rot) 0.4867 | loss(pos) 0.8652 | loss(seq) 0.0229 | grad 3.0473 | lr 0.0010 | time_forward 2.6780 | time_backward 3.7340 |
[2023-09-01 20:55:47,614::train::INFO] [train] Iter 02253 | loss 3.5389 | loss(rot) 2.1521 | loss(pos) 0.8314 | loss(seq) 0.5554 | grad 4.5787 | lr 0.0010 | time_forward 3.7190 | time_backward 4.7640 |
[2023-09-01 20:55:56,904::train::INFO] [train] Iter 02254 | loss 2.0113 | loss(rot) 0.8786 | loss(pos) 0.8439 | loss(seq) 0.2888 | grad 4.0755 | lr 0.0010 | time_forward 3.9350 | time_backward 5.3520 |
[2023-09-01 20:56:07,183::train::INFO] [train] Iter 02255 | loss 2.9769 | loss(rot) 2.7390 | loss(pos) 0.1549 | loss(seq) 0.0830 | grad 1.8579 | lr 0.0010 | time_forward 4.0530 | time_backward 6.2230 |
[2023-09-01 20:56:17,295::train::INFO] [train] Iter 02256 | loss 1.2668 | loss(rot) 0.4736 | loss(pos) 0.5066 | loss(seq) 0.2865 | grad 4.2500 | lr 0.0010 | time_forward 4.0920 | time_backward 6.0150 |
[2023-09-01 20:56:23,344::train::INFO] [train] Iter 02257 | loss 2.9691 | loss(rot) 2.4527 | loss(pos) 0.2339 | loss(seq) 0.2824 | grad 4.1344 | lr 0.0010 | time_forward 2.4980 | time_backward 3.5470 |
[2023-09-01 20:56:33,128::train::INFO] [train] Iter 02258 | loss 2.5863 | loss(rot) 1.7502 | loss(pos) 0.2691 | loss(seq) 0.5669 | grad 4.9101 | lr 0.0010 | time_forward 4.1260 | time_backward 5.6550 |
[2023-09-01 20:56:43,313::train::INFO] [train] Iter 02259 | loss 3.0877 | loss(rot) 1.9406 | loss(pos) 0.5943 | loss(seq) 0.5528 | grad 3.1959 | lr 0.0010 | time_forward 4.1920 | time_backward 5.9690 |
[2023-09-01 20:56:53,486::train::INFO] [train] Iter 02260 | loss 3.4105 | loss(rot) 3.1834 | loss(pos) 0.2239 | loss(seq) 0.0032 | grad 3.0966 | lr 0.0010 | time_forward 4.0260 | time_backward 6.1420 |
[2023-09-01 20:57:00,616::train::INFO] [train] Iter 02261 | loss 3.1368 | loss(rot) 3.0457 | loss(pos) 0.0911 | loss(seq) 0.0000 | grad 2.0445 | lr 0.0010 | time_forward 2.9740 | time_backward 4.1390 |
[2023-09-01 20:57:03,326::train::INFO] [train] Iter 02262 | loss 2.2092 | loss(rot) 1.1515 | loss(pos) 0.6569 | loss(seq) 0.4008 | grad 3.6673 | lr 0.0010 | time_forward 1.2450 | time_backward 1.4610 |
[2023-09-01 20:57:13,333::train::INFO] [train] Iter 02263 | loss 2.4748 | loss(rot) 1.4981 | loss(pos) 0.4597 | loss(seq) 0.5169 | grad 3.8795 | lr 0.0010 | time_forward 4.1520 | time_backward 5.8510 |
[2023-09-01 20:57:21,814::train::INFO] [train] Iter 02264 | loss 2.0618 | loss(rot) 1.3428 | loss(pos) 0.2573 | loss(seq) 0.4617 | grad 5.4405 | lr 0.0010 | time_forward 3.4770 | time_backward 5.0000 |
[2023-09-01 20:57:24,189::train::INFO] [train] Iter 02265 | loss 2.6447 | loss(rot) 1.5014 | loss(pos) 0.5193 | loss(seq) 0.6240 | grad 3.7808 | lr 0.0010 | time_forward 1.0830 | time_backward 1.2880 |
[2023-09-01 20:57:27,186::train::INFO] [train] Iter 02266 | loss 1.9663 | loss(rot) 0.1911 | loss(pos) 1.4329 | loss(seq) 0.3423 | grad 5.9205 | lr 0.0010 | time_forward 1.4830 | time_backward 1.5100 |
[2023-09-01 20:57:30,198::train::INFO] [train] Iter 02267 | loss 1.2690 | loss(rot) 0.4746 | loss(pos) 0.5730 | loss(seq) 0.2214 | grad 5.3188 | lr 0.0010 | time_forward 1.3900 | time_backward 1.6180 |
[2023-09-01 20:57:39,206::train::INFO] [train] Iter 02268 | loss 1.5395 | loss(rot) 0.6219 | loss(pos) 0.4734 | loss(seq) 0.4442 | grad 2.6750 | lr 0.0010 | time_forward 3.6960 | time_backward 5.2640 |
[2023-09-01 20:57:49,617::train::INFO] [train] Iter 02269 | loss 2.6644 | loss(rot) 1.6700 | loss(pos) 0.5032 | loss(seq) 0.4912 | grad 3.4451 | lr 0.0010 | time_forward 4.2910 | time_backward 6.1160 |
[2023-09-01 20:57:58,120::train::INFO] [train] Iter 02270 | loss 3.0859 | loss(rot) 2.7390 | loss(pos) 0.3228 | loss(seq) 0.0241 | grad 4.1480 | lr 0.0010 | time_forward 3.5520 | time_backward 4.9370 |
[2023-09-01 20:58:07,096::train::INFO] [train] Iter 02271 | loss 2.6202 | loss(rot) 1.1563 | loss(pos) 1.0649 | loss(seq) 0.3990 | grad 6.3966 | lr 0.0010 | time_forward 3.7490 | time_backward 5.2230 |
[2023-09-01 20:58:10,282::train::INFO] [train] Iter 02272 | loss 3.2350 | loss(rot) 2.6448 | loss(pos) 0.2574 | loss(seq) 0.3328 | grad 2.4121 | lr 0.0010 | time_forward 1.4520 | time_backward 1.7300 |
[2023-09-01 20:58:12,986::train::INFO] [train] Iter 02273 | loss 3.5349 | loss(rot) 3.2382 | loss(pos) 0.2954 | loss(seq) 0.0013 | grad 3.4875 | lr 0.0010 | time_forward 1.2550 | time_backward 1.4460 |
[2023-09-01 20:58:23,524::train::INFO] [train] Iter 02274 | loss 2.8640 | loss(rot) 2.5216 | loss(pos) 0.2553 | loss(seq) 0.0871 | grad 3.1088 | lr 0.0010 | time_forward 4.1950 | time_backward 6.3400 |
[2023-09-01 20:58:33,599::train::INFO] [train] Iter 02275 | loss 2.1512 | loss(rot) 0.0659 | loss(pos) 2.0773 | loss(seq) 0.0080 | grad 4.9346 | lr 0.0010 | time_forward 4.1680 | time_backward 5.9030 |
[2023-09-01 20:58:36,397::train::INFO] [train] Iter 02276 | loss 2.1448 | loss(rot) 0.8683 | loss(pos) 0.4915 | loss(seq) 0.7850 | grad 3.4718 | lr 0.0010 | time_forward 1.3190 | time_backward 1.4760 |
[2023-09-01 20:58:44,993::train::INFO] [train] Iter 02277 | loss 1.1075 | loss(rot) 0.3669 | loss(pos) 0.6769 | loss(seq) 0.0636 | grad 4.5102 | lr 0.0010 | time_forward 3.5390 | time_backward 5.0270 |
[2023-09-01 20:58:48,227::train::INFO] [train] Iter 02278 | loss 2.8926 | loss(rot) 1.5641 | loss(pos) 0.9041 | loss(seq) 0.4243 | grad 4.4734 | lr 0.0010 | time_forward 1.4570 | time_backward 1.7740 |
[2023-09-01 20:58:58,541::train::INFO] [train] Iter 02279 | loss 2.6417 | loss(rot) 1.7688 | loss(pos) 0.4308 | loss(seq) 0.4421 | grad 3.1968 | lr 0.0010 | time_forward 4.1500 | time_backward 6.1600 |
[2023-09-01 20:59:01,271::train::INFO] [train] Iter 02280 | loss 2.9717 | loss(rot) 2.2250 | loss(pos) 0.1970 | loss(seq) 0.5497 | grad 3.1959 | lr 0.0010 | time_forward 1.3250 | time_backward 1.4010 |
[2023-09-01 20:59:03,979::train::INFO] [train] Iter 02281 | loss 2.9787 | loss(rot) 2.3175 | loss(pos) 0.2968 | loss(seq) 0.3645 | grad 3.6266 | lr 0.0010 | time_forward 1.2270 | time_backward 1.4550 |
[2023-09-01 20:59:10,095::train::INFO] [train] Iter 02282 | loss 3.2869 | loss(rot) 2.7390 | loss(pos) 0.4340 | loss(seq) 0.1140 | grad 4.0135 | lr 0.0010 | time_forward 2.6620 | time_backward 3.4500 |
[2023-09-01 20:59:12,827::train::INFO] [train] Iter 02283 | loss 2.0114 | loss(rot) 1.1688 | loss(pos) 0.4621 | loss(seq) 0.3806 | grad 4.3921 | lr 0.0010 | time_forward 1.2560 | time_backward 1.4720 |
[2023-09-01 20:59:22,096::train::INFO] [train] Iter 02284 | loss 2.8089 | loss(rot) 2.6350 | loss(pos) 0.1649 | loss(seq) 0.0090 | grad 3.1347 | lr 0.0010 | time_forward 4.0070 | time_backward 5.2590 |
[2023-09-01 20:59:30,757::train::INFO] [train] Iter 02285 | loss 2.2326 | loss(rot) 1.2014 | loss(pos) 0.3375 | loss(seq) 0.6937 | grad 3.7646 | lr 0.0010 | time_forward 3.6210 | time_backward 5.0370 |
[2023-09-01 20:59:40,141::train::INFO] [train] Iter 02286 | loss 1.7075 | loss(rot) 0.5998 | loss(pos) 0.6406 | loss(seq) 0.4671 | grad 3.8243 | lr 0.0010 | time_forward 4.3250 | time_backward 5.0560 |
[2023-09-01 20:59:50,170::train::INFO] [train] Iter 02287 | loss 2.9962 | loss(rot) 2.0647 | loss(pos) 0.5018 | loss(seq) 0.4296 | grad 3.3142 | lr 0.0010 | time_forward 4.0220 | time_backward 6.0030 |
[2023-09-01 21:00:00,573::train::INFO] [train] Iter 02288 | loss 2.7883 | loss(rot) 2.0866 | loss(pos) 0.3090 | loss(seq) 0.3928 | grad 2.9330 | lr 0.0010 | time_forward 4.1340 | time_backward 6.2650 |
[2023-09-01 21:00:10,329::train::INFO] [train] Iter 02289 | loss 1.4957 | loss(rot) 0.5747 | loss(pos) 0.3507 | loss(seq) 0.5702 | grad 3.8957 | lr 0.0010 | time_forward 4.0710 | time_backward 5.6820 |
[2023-09-01 21:00:13,215::train::INFO] [train] Iter 02290 | loss 1.4609 | loss(rot) 0.3555 | loss(pos) 1.0881 | loss(seq) 0.0173 | grad 4.7559 | lr 0.0010 | time_forward 1.3410 | time_backward 1.5420 |
[2023-09-01 21:00:16,303::train::INFO] [train] Iter 02291 | loss 3.1387 | loss(rot) 2.4620 | loss(pos) 0.1880 | loss(seq) 0.4888 | grad 2.6508 | lr 0.0010 | time_forward 1.4470 | time_backward 1.6140 |
[2023-09-01 21:00:27,186::train::INFO] [train] Iter 02292 | loss 2.6401 | loss(rot) 2.4937 | loss(pos) 0.0608 | loss(seq) 0.0855 | grad 3.0240 | lr 0.0010 | time_forward 4.4080 | time_backward 6.4720 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.