text stringlengths 56 1.16k |
|---|
[2023-10-24 12:30:50,283::train::INFO] [train] Iter 583179 | loss 0.2938 | loss(rot) 0.0945 | loss(pos) 0.0066 | loss(seq) 0.1927 | grad 1.8623 | lr 0.0000 | time_forward 3.3360 | time_backward 4.5620 |
[2023-10-24 12:30:53,515::train::INFO] [train] Iter 583180 | loss 0.5210 | loss(rot) 0.0494 | loss(pos) 0.4630 | loss(seq) 0.0086 | grad 5.7416 | lr 0.0000 | time_forward 1.4720 | time_backward 1.7580 |
[2023-10-24 12:31:02,654::train::INFO] [train] Iter 583181 | loss 0.5809 | loss(rot) 0.5601 | loss(pos) 0.0186 | loss(seq) 0.0021 | grad 12.4599 | lr 0.0000 | time_forward 3.8410 | time_backward 5.2830 |
[2023-10-24 12:31:12,667::train::INFO] [train] Iter 583182 | loss 0.8611 | loss(rot) 0.7786 | loss(pos) 0.0355 | loss(seq) 0.0470 | grad 5.0977 | lr 0.0000 | time_forward 4.0680 | time_backward 5.9430 |
[2023-10-24 12:31:15,405::train::INFO] [train] Iter 583183 | loss 0.3950 | loss(rot) 0.0462 | loss(pos) 0.0287 | loss(seq) 0.3201 | grad 2.5343 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4290 |
[2023-10-24 12:31:25,391::train::INFO] [train] Iter 583184 | loss 1.8869 | loss(rot) 1.8021 | loss(pos) 0.0406 | loss(seq) 0.0442 | grad 3.6167 | lr 0.0000 | time_forward 4.2290 | time_backward 5.7550 |
[2023-10-24 12:31:28,139::train::INFO] [train] Iter 583185 | loss 0.0796 | loss(rot) 0.0458 | loss(pos) 0.0337 | loss(seq) 0.0001 | grad 1.3687 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4340 |
[2023-10-24 12:31:31,530::train::INFO] [train] Iter 583186 | loss 0.9727 | loss(rot) 0.0778 | loss(pos) 0.8920 | loss(seq) 0.0029 | grad 8.9165 | lr 0.0000 | time_forward 1.5290 | time_backward 1.8590 |
[2023-10-24 12:31:41,394::train::INFO] [train] Iter 583187 | loss 0.2130 | loss(rot) 0.1926 | loss(pos) 0.0084 | loss(seq) 0.0120 | grad 1.8778 | lr 0.0000 | time_forward 4.0090 | time_backward 5.8530 |
[2023-10-24 12:31:50,084::train::INFO] [train] Iter 583188 | loss 1.5922 | loss(rot) 1.0811 | loss(pos) 0.1421 | loss(seq) 0.3690 | grad 5.0165 | lr 0.0000 | time_forward 3.6790 | time_backward 4.9960 |
[2023-10-24 12:31:58,138::train::INFO] [train] Iter 583189 | loss 0.6473 | loss(rot) 0.1955 | loss(pos) 0.0526 | loss(seq) 0.3992 | grad 3.4658 | lr 0.0000 | time_forward 3.3620 | time_backward 4.6880 |
[2023-10-24 12:32:00,876::train::INFO] [train] Iter 583190 | loss 0.6378 | loss(rot) 0.1081 | loss(pos) 0.5224 | loss(seq) 0.0073 | grad 4.5267 | lr 0.0000 | time_forward 1.3000 | time_backward 1.4340 |
[2023-10-24 12:32:11,399::train::INFO] [train] Iter 583191 | loss 0.4203 | loss(rot) 0.3873 | loss(pos) 0.0304 | loss(seq) 0.0027 | grad 2.8306 | lr 0.0000 | time_forward 4.1680 | time_backward 6.3530 |
[2023-10-24 12:32:21,525::train::INFO] [train] Iter 583192 | loss 0.6851 | loss(rot) 0.0408 | loss(pos) 0.6381 | loss(seq) 0.0062 | grad 8.8164 | lr 0.0000 | time_forward 4.0840 | time_backward 6.0380 |
[2023-10-24 12:32:29,064::train::INFO] [train] Iter 583193 | loss 0.6825 | loss(rot) 0.5242 | loss(pos) 0.0101 | loss(seq) 0.1482 | grad 1.8138 | lr 0.0000 | time_forward 3.1580 | time_backward 4.3790 |
[2023-10-24 12:32:39,511::train::INFO] [train] Iter 583194 | loss 0.3837 | loss(rot) 0.0397 | loss(pos) 0.3394 | loss(seq) 0.0046 | grad 4.9728 | lr 0.0000 | time_forward 4.1330 | time_backward 6.3110 |
[2023-10-24 12:32:49,884::train::INFO] [train] Iter 583195 | loss 0.8684 | loss(rot) 0.3130 | loss(pos) 0.0921 | loss(seq) 0.4633 | grad 3.6630 | lr 0.0000 | time_forward 4.3350 | time_backward 6.0350 |
[2023-10-24 12:32:52,817::train::INFO] [train] Iter 583196 | loss 0.5979 | loss(rot) 0.4543 | loss(pos) 0.0877 | loss(seq) 0.0559 | grad 3.1187 | lr 0.0000 | time_forward 1.3180 | time_backward 1.6120 |
[2023-10-24 12:33:01,059::train::INFO] [train] Iter 583197 | loss 2.1119 | loss(rot) 1.9174 | loss(pos) 0.0492 | loss(seq) 0.1453 | grad 37.6082 | lr 0.0000 | time_forward 3.4760 | time_backward 4.7620 |
[2023-10-24 12:33:11,035::train::INFO] [train] Iter 583198 | loss 0.6993 | loss(rot) 0.4167 | loss(pos) 0.1848 | loss(seq) 0.0979 | grad 3.0860 | lr 0.0000 | time_forward 4.2040 | time_backward 5.7700 |
[2023-10-24 12:33:20,103::train::INFO] [train] Iter 583199 | loss 0.7359 | loss(rot) 0.5773 | loss(pos) 0.0219 | loss(seq) 0.1367 | grad 2.5329 | lr 0.0000 | time_forward 3.7920 | time_backward 5.2730 |
[2023-10-24 12:33:28,437::train::INFO] [train] Iter 583200 | loss 1.3443 | loss(rot) 0.9629 | loss(pos) 0.0724 | loss(seq) 0.3089 | grad 29.5761 | lr 0.0000 | time_forward 3.4830 | time_backward 4.8480 |
[2023-10-24 12:33:37,166::train::INFO] [train] Iter 583201 | loss 1.0350 | loss(rot) 0.2639 | loss(pos) 0.5180 | loss(seq) 0.2531 | grad 5.0481 | lr 0.0000 | time_forward 3.6440 | time_backward 5.0820 |
[2023-10-24 12:33:46,277::train::INFO] [train] Iter 583202 | loss 0.4241 | loss(rot) 0.0919 | loss(pos) 0.0284 | loss(seq) 0.3038 | grad 2.3972 | lr 0.0000 | time_forward 3.8460 | time_backward 5.2630 |
[2023-10-24 12:33:56,225::train::INFO] [train] Iter 583203 | loss 0.2423 | loss(rot) 0.1902 | loss(pos) 0.0259 | loss(seq) 0.0262 | grad 2.2361 | lr 0.0000 | time_forward 4.2020 | time_backward 5.7420 |
[2023-10-24 12:34:04,926::train::INFO] [train] Iter 583204 | loss 2.1001 | loss(rot) 2.0530 | loss(pos) 0.0219 | loss(seq) 0.0251 | grad 5.7476 | lr 0.0000 | time_forward 3.6750 | time_backward 5.0230 |
[2023-10-24 12:34:07,762::train::INFO] [train] Iter 583205 | loss 0.2021 | loss(rot) 0.0400 | loss(pos) 0.1526 | loss(seq) 0.0095 | grad 2.9338 | lr 0.0000 | time_forward 1.3100 | time_backward 1.5220 |
[2023-10-24 12:34:10,570::train::INFO] [train] Iter 583206 | loss 0.7365 | loss(rot) 0.5222 | loss(pos) 0.0283 | loss(seq) 0.1860 | grad 3.1525 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4720 |
[2023-10-24 12:34:13,431::train::INFO] [train] Iter 583207 | loss 1.6853 | loss(rot) 1.5961 | loss(pos) 0.0597 | loss(seq) 0.0295 | grad 48.6989 | lr 0.0000 | time_forward 1.3710 | time_backward 1.4870 |
[2023-10-24 12:34:16,878::train::INFO] [train] Iter 583208 | loss 1.1570 | loss(rot) 1.0635 | loss(pos) 0.0380 | loss(seq) 0.0555 | grad 3.0665 | lr 0.0000 | time_forward 1.5190 | time_backward 1.8930 |
[2023-10-24 12:34:26,798::train::INFO] [train] Iter 583209 | loss 0.4493 | loss(rot) 0.1236 | loss(pos) 0.0562 | loss(seq) 0.2695 | grad 2.3807 | lr 0.0000 | time_forward 3.9720 | time_backward 5.9460 |
[2023-10-24 12:34:29,525::train::INFO] [train] Iter 583210 | loss 0.2489 | loss(rot) 0.1445 | loss(pos) 0.0211 | loss(seq) 0.0832 | grad 2.6791 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4170 |
[2023-10-24 12:34:39,511::train::INFO] [train] Iter 583211 | loss 0.5294 | loss(rot) 0.1647 | loss(pos) 0.0874 | loss(seq) 0.2773 | grad 3.0952 | lr 0.0000 | time_forward 4.1190 | time_backward 5.8640 |
[2023-10-24 12:34:48,171::train::INFO] [train] Iter 583212 | loss 0.6608 | loss(rot) 0.6041 | loss(pos) 0.0254 | loss(seq) 0.0312 | grad 9.9851 | lr 0.0000 | time_forward 3.6490 | time_backward 5.0080 |
[2023-10-24 12:34:56,456::train::INFO] [train] Iter 583213 | loss 0.2665 | loss(rot) 0.1299 | loss(pos) 0.0274 | loss(seq) 0.1092 | grad 1.8636 | lr 0.0000 | time_forward 3.4850 | time_backward 4.7970 |
[2023-10-24 12:35:04,543::train::INFO] [train] Iter 583214 | loss 0.9582 | loss(rot) 0.0107 | loss(pos) 0.9472 | loss(seq) 0.0003 | grad 21.3546 | lr 0.0000 | time_forward 3.3920 | time_backward 4.6920 |
[2023-10-24 12:35:14,448::train::INFO] [train] Iter 583215 | loss 0.7788 | loss(rot) 0.6794 | loss(pos) 0.0389 | loss(seq) 0.0605 | grad 13.9569 | lr 0.0000 | time_forward 4.1470 | time_backward 5.7540 |
[2023-10-24 12:35:22,288::train::INFO] [train] Iter 583216 | loss 0.0932 | loss(rot) 0.0705 | loss(pos) 0.0227 | loss(seq) 0.0000 | grad 2.2812 | lr 0.0000 | time_forward 3.3080 | time_backward 4.5290 |
[2023-10-24 12:35:28,843::train::INFO] [train] Iter 583217 | loss 0.2618 | loss(rot) 0.1081 | loss(pos) 0.1253 | loss(seq) 0.0284 | grad 3.6475 | lr 0.0000 | time_forward 2.8180 | time_backward 3.7350 |
[2023-10-24 12:35:31,698::train::INFO] [train] Iter 583218 | loss 0.3487 | loss(rot) 0.2969 | loss(pos) 0.0512 | loss(seq) 0.0006 | grad 12.5059 | lr 0.0000 | time_forward 1.2850 | time_backward 1.5660 |
[2023-10-24 12:35:33,969::train::INFO] [train] Iter 583219 | loss 0.4567 | loss(rot) 0.3794 | loss(pos) 0.0584 | loss(seq) 0.0188 | grad 2.3063 | lr 0.0000 | time_forward 1.0420 | time_backward 1.2260 |
[2023-10-24 12:35:44,097::train::INFO] [train] Iter 583220 | loss 0.3186 | loss(rot) 0.0801 | loss(pos) 0.0905 | loss(seq) 0.1480 | grad 2.8282 | lr 0.0000 | time_forward 3.9780 | time_backward 6.1480 |
[2023-10-24 12:35:53,896::train::INFO] [train] Iter 583221 | loss 0.6199 | loss(rot) 0.4662 | loss(pos) 0.0463 | loss(seq) 0.1074 | grad 7.6187 | lr 0.0000 | time_forward 3.9950 | time_backward 5.8000 |
[2023-10-24 12:36:03,949::train::INFO] [train] Iter 583222 | loss 0.8144 | loss(rot) 0.7400 | loss(pos) 0.0438 | loss(seq) 0.0305 | grad 4.0777 | lr 0.0000 | time_forward 4.1250 | time_backward 5.9250 |
[2023-10-24 12:36:06,419::train::INFO] [train] Iter 583223 | loss 0.6976 | loss(rot) 0.2422 | loss(pos) 0.1487 | loss(seq) 0.3067 | grad 3.8939 | lr 0.0000 | time_forward 1.2050 | time_backward 1.2610 |
[2023-10-24 12:36:12,881::train::INFO] [train] Iter 583224 | loss 0.3092 | loss(rot) 0.2042 | loss(pos) 0.0201 | loss(seq) 0.0849 | grad 2.1214 | lr 0.0000 | time_forward 2.7620 | time_backward 3.6970 |
[2023-10-24 12:36:15,631::train::INFO] [train] Iter 583225 | loss 1.0413 | loss(rot) 0.0079 | loss(pos) 1.0321 | loss(seq) 0.0012 | grad 9.3949 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4410 |
[2023-10-24 12:36:17,934::train::INFO] [train] Iter 583226 | loss 1.1528 | loss(rot) 0.7587 | loss(pos) 0.1186 | loss(seq) 0.2755 | grad 4.9443 | lr 0.0000 | time_forward 1.0420 | time_backward 1.2340 |
[2023-10-24 12:36:25,270::train::INFO] [train] Iter 583227 | loss 0.2665 | loss(rot) 0.0993 | loss(pos) 0.1320 | loss(seq) 0.0351 | grad 4.8799 | lr 0.0000 | time_forward 3.1080 | time_backward 4.2230 |
[2023-10-24 12:36:34,268::train::INFO] [train] Iter 583228 | loss 0.9990 | loss(rot) 0.0076 | loss(pos) 0.9882 | loss(seq) 0.0032 | grad 10.3258 | lr 0.0000 | time_forward 3.7910 | time_backward 5.2040 |
[2023-10-24 12:36:42,462::train::INFO] [train] Iter 583229 | loss 1.0873 | loss(rot) 0.7987 | loss(pos) 0.0945 | loss(seq) 0.1941 | grad 4.7032 | lr 0.0000 | time_forward 3.4430 | time_backward 4.7480 |
[2023-10-24 12:36:51,043::train::INFO] [train] Iter 583230 | loss 0.7862 | loss(rot) 0.7510 | loss(pos) 0.0106 | loss(seq) 0.0245 | grad 3.6088 | lr 0.0000 | time_forward 3.5900 | time_backward 4.9890 |
[2023-10-24 12:36:59,113::train::INFO] [train] Iter 583231 | loss 0.3053 | loss(rot) 0.0797 | loss(pos) 0.0316 | loss(seq) 0.1940 | grad 2.0486 | lr 0.0000 | time_forward 3.3620 | time_backward 4.7040 |
[2023-10-24 12:37:01,849::train::INFO] [train] Iter 583232 | loss 0.5590 | loss(rot) 0.5398 | loss(pos) 0.0177 | loss(seq) 0.0015 | grad 3.2805 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4220 |
[2023-10-24 12:37:04,640::train::INFO] [train] Iter 583233 | loss 0.5648 | loss(rot) 0.0740 | loss(pos) 0.0644 | loss(seq) 0.4265 | grad 2.8576 | lr 0.0000 | time_forward 1.3580 | time_backward 1.4300 |
[2023-10-24 12:37:13,757::train::INFO] [train] Iter 583234 | loss 1.1116 | loss(rot) 0.8415 | loss(pos) 0.0422 | loss(seq) 0.2279 | grad 3.4415 | lr 0.0000 | time_forward 3.8800 | time_backward 5.2340 |
[2023-10-24 12:37:23,577::train::INFO] [train] Iter 583235 | loss 0.8764 | loss(rot) 0.1767 | loss(pos) 0.3269 | loss(seq) 0.3728 | grad 5.1337 | lr 0.0000 | time_forward 3.9920 | time_backward 5.8250 |
[2023-10-24 12:37:32,631::train::INFO] [train] Iter 583236 | loss 1.7690 | loss(rot) 1.5811 | loss(pos) 0.0576 | loss(seq) 0.1303 | grad 4.6984 | lr 0.0000 | time_forward 3.8500 | time_backward 5.2000 |
[2023-10-24 12:37:40,579::train::INFO] [train] Iter 583237 | loss 0.4234 | loss(rot) 0.3082 | loss(pos) 0.0163 | loss(seq) 0.0989 | grad 2.9281 | lr 0.0000 | time_forward 3.3550 | time_backward 4.5890 |
[2023-10-24 12:37:50,387::train::INFO] [train] Iter 583238 | loss 0.4919 | loss(rot) 0.2147 | loss(pos) 0.0937 | loss(seq) 0.1835 | grad 3.9730 | lr 0.0000 | time_forward 3.9450 | time_backward 5.8590 |
[2023-10-24 12:37:58,137::train::INFO] [train] Iter 583239 | loss 0.4753 | loss(rot) 0.3303 | loss(pos) 0.0084 | loss(seq) 0.1365 | grad 4.5523 | lr 0.0000 | time_forward 3.2720 | time_backward 4.4750 |
[2023-10-24 12:38:06,643::train::INFO] [train] Iter 583240 | loss 0.6441 | loss(rot) 0.2670 | loss(pos) 0.2447 | loss(seq) 0.1324 | grad 5.0084 | lr 0.0000 | time_forward 3.5840 | time_backward 4.9170 |
[2023-10-24 12:38:15,698::train::INFO] [train] Iter 583241 | loss 1.3646 | loss(rot) 1.0697 | loss(pos) 0.0239 | loss(seq) 0.2710 | grad 6.2494 | lr 0.0000 | time_forward 3.8190 | time_backward 5.2330 |
[2023-10-24 12:38:25,560::train::INFO] [train] Iter 583242 | loss 0.8251 | loss(rot) 0.4120 | loss(pos) 0.0754 | loss(seq) 0.3377 | grad 2.5245 | lr 0.0000 | time_forward 4.1330 | time_backward 5.7260 |
[2023-10-24 12:38:35,392::train::INFO] [train] Iter 583243 | loss 0.3736 | loss(rot) 0.1806 | loss(pos) 0.1279 | loss(seq) 0.0651 | grad 3.3727 | lr 0.0000 | time_forward 3.9780 | time_backward 5.8500 |
[2023-10-24 12:38:44,073::train::INFO] [train] Iter 583244 | loss 0.6618 | loss(rot) 0.0894 | loss(pos) 0.2450 | loss(seq) 0.3274 | grad 4.7473 | lr 0.0000 | time_forward 3.6650 | time_backward 5.0120 |
[2023-10-24 12:38:53,932::train::INFO] [train] Iter 583245 | loss 1.3828 | loss(rot) 1.3378 | loss(pos) 0.0286 | loss(seq) 0.0165 | grad 7.7402 | lr 0.0000 | time_forward 4.0260 | time_backward 5.8290 |
[2023-10-24 12:39:02,269::train::INFO] [train] Iter 583246 | loss 1.3764 | loss(rot) 0.7518 | loss(pos) 0.0838 | loss(seq) 0.5407 | grad 6.6441 | lr 0.0000 | time_forward 3.5340 | time_backward 4.8000 |
[2023-10-24 12:39:04,991::train::INFO] [train] Iter 583247 | loss 1.4071 | loss(rot) 0.9674 | loss(pos) 0.1859 | loss(seq) 0.2538 | grad 4.4674 | lr 0.0000 | time_forward 1.2830 | time_backward 1.4350 |
[2023-10-24 12:39:07,860::train::INFO] [train] Iter 583248 | loss 0.9579 | loss(rot) 0.2178 | loss(pos) 0.2786 | loss(seq) 0.4615 | grad 5.2928 | lr 0.0000 | time_forward 1.3540 | time_backward 1.5120 |
[2023-10-24 12:39:15,757::train::INFO] [train] Iter 583249 | loss 0.2677 | loss(rot) 0.2206 | loss(pos) 0.0197 | loss(seq) 0.0275 | grad 3.0365 | lr 0.0000 | time_forward 3.3280 | time_backward 4.5660 |
[2023-10-24 12:39:24,208::train::INFO] [train] Iter 583250 | loss 1.6624 | loss(rot) 1.0753 | loss(pos) 0.1847 | loss(seq) 0.4024 | grad 6.1894 | lr 0.0000 | time_forward 3.4870 | time_backward 4.9620 |
[2023-10-24 12:39:27,283::train::INFO] [train] Iter 583251 | loss 1.0719 | loss(rot) 0.0139 | loss(pos) 1.0558 | loss(seq) 0.0022 | grad 9.6793 | lr 0.0000 | time_forward 1.4970 | time_backward 1.5730 |
[2023-10-24 12:39:38,310::train::INFO] [train] Iter 583252 | loss 0.5396 | loss(rot) 0.1899 | loss(pos) 0.0882 | loss(seq) 0.2615 | grad 3.1913 | lr 0.0000 | time_forward 4.7770 | time_backward 6.2470 |
[2023-10-24 12:39:49,199::train::INFO] [train] Iter 583253 | loss 1.3057 | loss(rot) 1.2845 | loss(pos) 0.0198 | loss(seq) 0.0013 | grad 10.5302 | lr 0.0000 | time_forward 4.5340 | time_backward 6.3510 |
[2023-10-24 12:39:51,968::train::INFO] [train] Iter 583254 | loss 0.3224 | loss(rot) 0.2877 | loss(pos) 0.0253 | loss(seq) 0.0093 | grad 7.5818 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4400 |
[2023-10-24 12:40:01,341::train::INFO] [train] Iter 583255 | loss 0.3265 | loss(rot) 0.0613 | loss(pos) 0.0637 | loss(seq) 0.2016 | grad 3.0307 | lr 0.0000 | time_forward 3.9520 | time_backward 5.3910 |
[2023-10-24 12:40:09,228::train::INFO] [train] Iter 583256 | loss 1.0873 | loss(rot) 1.0685 | loss(pos) 0.0185 | loss(seq) 0.0003 | grad 17.9147 | lr 0.0000 | time_forward 3.4600 | time_backward 4.4240 |
[2023-10-24 12:40:18,104::train::INFO] [train] Iter 583257 | loss 0.4510 | loss(rot) 0.0200 | loss(pos) 0.4296 | loss(seq) 0.0015 | grad 7.0102 | lr 0.0000 | time_forward 3.7250 | time_backward 5.1480 |
[2023-10-24 12:40:28,282::train::INFO] [train] Iter 583258 | loss 0.7837 | loss(rot) 0.7351 | loss(pos) 0.0340 | loss(seq) 0.0147 | grad 3.4384 | lr 0.0000 | time_forward 4.1590 | time_backward 6.0160 |
[2023-10-24 12:40:38,400::train::INFO] [train] Iter 583259 | loss 0.7404 | loss(rot) 0.2456 | loss(pos) 0.1319 | loss(seq) 0.3629 | grad 2.7832 | lr 0.0000 | time_forward 4.2300 | time_backward 5.8860 |
[2023-10-24 12:40:41,953::train::INFO] [train] Iter 583260 | loss 0.7591 | loss(rot) 0.3438 | loss(pos) 0.0363 | loss(seq) 0.3790 | grad 3.2829 | lr 0.0000 | time_forward 1.5440 | time_backward 2.0060 |
[2023-10-24 12:40:50,703::train::INFO] [train] Iter 583261 | loss 0.1637 | loss(rot) 0.1085 | loss(pos) 0.0295 | loss(seq) 0.0256 | grad 2.2021 | lr 0.0000 | time_forward 3.7240 | time_backward 5.0100 |
[2023-10-24 12:41:01,037::train::INFO] [train] Iter 583262 | loss 1.7947 | loss(rot) 1.3426 | loss(pos) 0.1493 | loss(seq) 0.3028 | grad 3.7082 | lr 0.0000 | time_forward 4.2110 | time_backward 6.1190 |
[2023-10-24 12:41:11,705::train::INFO] [train] Iter 583263 | loss 1.0931 | loss(rot) 0.6339 | loss(pos) 0.2136 | loss(seq) 0.2455 | grad 3.0339 | lr 0.0000 | time_forward 4.4460 | time_backward 6.2180 |
[2023-10-24 12:41:20,926::train::INFO] [train] Iter 583264 | loss 0.5168 | loss(rot) 0.0137 | loss(pos) 0.4999 | loss(seq) 0.0032 | grad 6.5709 | lr 0.0000 | time_forward 3.9480 | time_backward 5.2700 |
[2023-10-24 12:41:23,319::train::INFO] [train] Iter 583265 | loss 1.8431 | loss(rot) 1.2579 | loss(pos) 0.1203 | loss(seq) 0.4649 | grad 3.9225 | lr 0.0000 | time_forward 1.1180 | time_backward 1.2720 |
[2023-10-24 12:41:26,138::train::INFO] [train] Iter 583266 | loss 0.4074 | loss(rot) 0.1237 | loss(pos) 0.0455 | loss(seq) 0.2382 | grad 2.4238 | lr 0.0000 | time_forward 1.2910 | time_backward 1.5240 |
[2023-10-24 12:41:34,762::train::INFO] [train] Iter 583267 | loss 0.4934 | loss(rot) 0.2835 | loss(pos) 0.1303 | loss(seq) 0.0797 | grad 3.1845 | lr 0.0000 | time_forward 3.5760 | time_backward 5.0440 |
[2023-10-24 12:41:37,543::train::INFO] [train] Iter 583268 | loss 0.5926 | loss(rot) 0.5301 | loss(pos) 0.0315 | loss(seq) 0.0311 | grad 3.3417 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4640 |
[2023-10-24 12:41:46,567::train::INFO] [train] Iter 583269 | loss 0.2763 | loss(rot) 0.1117 | loss(pos) 0.1512 | loss(seq) 0.0134 | grad 3.6148 | lr 0.0000 | time_forward 3.8270 | time_backward 5.1610 |
[2023-10-24 12:41:54,095::train::INFO] [train] Iter 583270 | loss 0.3680 | loss(rot) 0.0667 | loss(pos) 0.2938 | loss(seq) 0.0075 | grad 6.8241 | lr 0.0000 | time_forward 3.2290 | time_backward 4.2970 |
[2023-10-24 12:41:56,898::train::INFO] [train] Iter 583271 | loss 0.4462 | loss(rot) 0.1772 | loss(pos) 0.0607 | loss(seq) 0.2082 | grad 3.4186 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4930 |
[2023-10-24 12:41:59,446::train::INFO] [train] Iter 583272 | loss 0.7947 | loss(rot) 0.1854 | loss(pos) 0.6089 | loss(seq) 0.0005 | grad 9.5309 | lr 0.0000 | time_forward 1.2100 | time_backward 1.3020 |
[2023-10-24 12:42:08,368::train::INFO] [train] Iter 583273 | loss 0.6021 | loss(rot) 0.1397 | loss(pos) 0.0424 | loss(seq) 0.4200 | grad 3.2297 | lr 0.0000 | time_forward 3.5340 | time_backward 5.3640 |
[2023-10-24 12:42:18,995::train::INFO] [train] Iter 583274 | loss 1.3174 | loss(rot) 0.7749 | loss(pos) 0.0393 | loss(seq) 0.5032 | grad 4.5431 | lr 0.0000 | time_forward 4.4080 | time_backward 6.2150 |
[2023-10-24 12:42:27,080::train::INFO] [train] Iter 583275 | loss 0.2290 | loss(rot) 0.0848 | loss(pos) 0.0329 | loss(seq) 0.1114 | grad 1.7793 | lr 0.0000 | time_forward 3.2970 | time_backward 4.7850 |
[2023-10-24 12:42:35,434::train::INFO] [train] Iter 583276 | loss 0.2737 | loss(rot) 0.0633 | loss(pos) 0.2018 | loss(seq) 0.0086 | grad 6.4658 | lr 0.0000 | time_forward 3.4560 | time_backward 4.8950 |
[2023-10-24 12:42:43,907::train::INFO] [train] Iter 583277 | loss 0.4564 | loss(rot) 0.1441 | loss(pos) 0.0923 | loss(seq) 0.2200 | grad 3.2526 | lr 0.0000 | time_forward 3.4030 | time_backward 5.0660 |
[2023-10-24 12:42:53,571::train::INFO] [train] Iter 583278 | loss 0.3440 | loss(rot) 0.1222 | loss(pos) 0.0264 | loss(seq) 0.1955 | grad 2.4338 | lr 0.0000 | time_forward 3.9850 | time_backward 5.6760 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.