text
stringlengths 56
1.16k
|
|---|
[2023-09-02 10:25:41,074::train::INFO] [train] Iter 08887 | loss 1.4792 | loss(rot) 0.5951 | loss(pos) 0.8702 | loss(seq) 0.0138 | grad 3.9849 | lr 0.0010 | time_forward 3.9190 | time_backward 5.3880 |
[2023-09-02 10:25:42,768::train::INFO] [train] Iter 08888 | loss 3.4615 | loss(rot) 2.1510 | loss(pos) 0.6559 | loss(seq) 0.6546 | grad 9.9352 | lr 0.0010 | time_forward 0.7740 | time_backward 0.9170 |
[2023-09-02 10:25:52,722::train::INFO] [train] Iter 08889 | loss 1.6821 | loss(rot) 0.0223 | loss(pos) 1.6535 | loss(seq) 0.0062 | grad 7.0673 | lr 0.0010 | time_forward 4.3120 | time_backward 5.6380 |
[2023-09-02 10:26:01,593::train::INFO] [train] Iter 08890 | loss 2.1639 | loss(rot) 1.8032 | loss(pos) 0.1586 | loss(seq) 0.2022 | grad 5.5844 | lr 0.0010 | time_forward 3.7500 | time_backward 5.1170 |
[2023-09-02 10:26:11,035::train::INFO] [train] Iter 08891 | loss 2.5708 | loss(rot) 1.6953 | loss(pos) 0.3277 | loss(seq) 0.5479 | grad 5.8949 | lr 0.0010 | time_forward 4.0410 | time_backward 5.3970 |
[2023-09-02 10:26:21,230::train::INFO] [train] Iter 08892 | loss 1.6427 | loss(rot) 1.4987 | loss(pos) 0.1020 | loss(seq) 0.0420 | grad 4.1607 | lr 0.0010 | time_forward 4.1460 | time_backward 6.0450 |
[2023-09-02 10:26:24,301::train::INFO] [train] Iter 08893 | loss 2.6385 | loss(rot) 2.2102 | loss(pos) 0.1196 | loss(seq) 0.3086 | grad 5.6052 | lr 0.0010 | time_forward 1.6670 | time_backward 1.4010 |
[2023-09-02 10:26:34,724::train::INFO] [train] Iter 08894 | loss 1.5026 | loss(rot) 0.4082 | loss(pos) 1.0643 | loss(seq) 0.0301 | grad 5.9846 | lr 0.0010 | time_forward 4.5770 | time_backward 5.8420 |
[2023-09-02 10:26:42,269::train::INFO] [train] Iter 08895 | loss 2.4999 | loss(rot) 1.7772 | loss(pos) 0.2754 | loss(seq) 0.4473 | grad 5.0950 | lr 0.0010 | time_forward 3.2460 | time_backward 4.2950 |
[2023-09-02 10:26:51,024::train::INFO] [train] Iter 08896 | loss 0.5855 | loss(rot) 0.2093 | loss(pos) 0.3001 | loss(seq) 0.0762 | grad 4.2028 | lr 0.0010 | time_forward 3.7650 | time_backward 4.9870 |
[2023-09-02 10:26:59,282::train::INFO] [train] Iter 08897 | loss 1.5004 | loss(rot) 1.3622 | loss(pos) 0.1220 | loss(seq) 0.0162 | grad 4.7466 | lr 0.0010 | time_forward 3.5150 | time_backward 4.7400 |
[2023-09-02 10:27:08,554::train::INFO] [train] Iter 08898 | loss 0.8591 | loss(rot) 0.0904 | loss(pos) 0.5626 | loss(seq) 0.2061 | grad 5.5094 | lr 0.0010 | time_forward 3.9760 | time_backward 5.2930 |
[2023-09-02 10:27:16,870::train::INFO] [train] Iter 08899 | loss 2.1724 | loss(rot) 1.0744 | loss(pos) 0.6067 | loss(seq) 0.4913 | grad 6.9268 | lr 0.0010 | time_forward 3.6120 | time_backward 4.6980 |
[2023-09-02 10:27:19,212::train::INFO] [train] Iter 08900 | loss 2.8306 | loss(rot) 1.9037 | loss(pos) 0.2974 | loss(seq) 0.6295 | grad 5.2581 | lr 0.0010 | time_forward 1.1000 | time_backward 1.2380 |
[2023-09-02 10:27:22,040::train::INFO] [train] Iter 08901 | loss 1.0863 | loss(rot) 0.3785 | loss(pos) 0.2085 | loss(seq) 0.4993 | grad 3.9238 | lr 0.0010 | time_forward 1.4080 | time_backward 1.4180 |
[2023-09-02 10:27:29,972::train::INFO] [train] Iter 08902 | loss 1.8234 | loss(rot) 1.3409 | loss(pos) 0.2844 | loss(seq) 0.1981 | grad 6.0410 | lr 0.0010 | time_forward 3.3570 | time_backward 4.5710 |
[2023-09-02 10:27:40,291::train::INFO] [train] Iter 08903 | loss 3.7185 | loss(rot) 0.1615 | loss(pos) 3.5570 | loss(seq) 0.0000 | grad 7.4461 | lr 0.0010 | time_forward 4.2320 | time_backward 6.0850 |
[2023-09-02 10:27:50,474::train::INFO] [train] Iter 08904 | loss 2.1106 | loss(rot) 1.4836 | loss(pos) 0.2444 | loss(seq) 0.3826 | grad 4.6334 | lr 0.0010 | time_forward 4.3180 | time_backward 5.8620 |
[2023-09-02 10:27:53,731::train::INFO] [train] Iter 08905 | loss 1.9850 | loss(rot) 1.7376 | loss(pos) 0.2058 | loss(seq) 0.0415 | grad 4.8107 | lr 0.0010 | time_forward 1.4110 | time_backward 1.8420 |
[2023-09-02 10:27:56,459::train::INFO] [train] Iter 08906 | loss 1.2186 | loss(rot) 0.6259 | loss(pos) 0.5359 | loss(seq) 0.0568 | grad 4.6516 | lr 0.0010 | time_forward 1.2730 | time_backward 1.4390 |
[2023-09-02 10:28:06,587::train::INFO] [train] Iter 08907 | loss 2.6243 | loss(rot) 2.2800 | loss(pos) 0.3431 | loss(seq) 0.0012 | grad 5.8296 | lr 0.0010 | time_forward 4.2730 | time_backward 5.8520 |
[2023-09-02 10:28:09,315::train::INFO] [train] Iter 08908 | loss 2.0408 | loss(rot) 1.8148 | loss(pos) 0.2257 | loss(seq) 0.0003 | grad 5.0923 | lr 0.0010 | time_forward 1.2950 | time_backward 1.4290 |
[2023-09-02 10:28:11,761::train::INFO] [train] Iter 08909 | loss 2.2378 | loss(rot) 1.7796 | loss(pos) 0.2620 | loss(seq) 0.1961 | grad 4.7939 | lr 0.0010 | time_forward 1.2000 | time_backward 1.2430 |
[2023-09-02 10:28:20,180::train::INFO] [train] Iter 08910 | loss 1.2341 | loss(rot) 0.2779 | loss(pos) 0.5991 | loss(seq) 0.3571 | grad 3.7320 | lr 0.0010 | time_forward 3.6440 | time_backward 4.7710 |
[2023-09-02 10:28:23,467::train::INFO] [train] Iter 08911 | loss 2.4096 | loss(rot) 1.7355 | loss(pos) 0.2785 | loss(seq) 0.3955 | grad 7.3868 | lr 0.0010 | time_forward 1.4300 | time_backward 1.8530 |
[2023-09-02 10:28:33,518::train::INFO] [train] Iter 08912 | loss 1.7068 | loss(rot) 1.1184 | loss(pos) 0.2264 | loss(seq) 0.3620 | grad 5.1801 | lr 0.0010 | time_forward 4.0720 | time_backward 5.9760 |
[2023-09-02 10:28:43,655::train::INFO] [train] Iter 08913 | loss 1.7187 | loss(rot) 0.5297 | loss(pos) 0.6467 | loss(seq) 0.5423 | grad 3.0581 | lr 0.0010 | time_forward 4.2130 | time_backward 5.9210 |
[2023-09-02 10:28:53,587::train::INFO] [train] Iter 08914 | loss 1.3701 | loss(rot) 0.7769 | loss(pos) 0.0976 | loss(seq) 0.4956 | grad 3.4907 | lr 0.0010 | time_forward 3.9640 | time_backward 5.9640 |
[2023-09-02 10:29:03,238::train::INFO] [train] Iter 08915 | loss 2.4982 | loss(rot) 1.7685 | loss(pos) 0.3569 | loss(seq) 0.3727 | grad 7.4715 | lr 0.0010 | time_forward 3.9240 | time_backward 5.7240 |
[2023-09-02 10:29:11,206::train::INFO] [train] Iter 08916 | loss 0.9646 | loss(rot) 0.1650 | loss(pos) 0.7475 | loss(seq) 0.0521 | grad 5.1856 | lr 0.0010 | time_forward 3.3250 | time_backward 4.6240 |
[2023-09-02 10:29:13,985::train::INFO] [train] Iter 08917 | loss 1.7034 | loss(rot) 0.9911 | loss(pos) 0.1961 | loss(seq) 0.5163 | grad 3.6633 | lr 0.0010 | time_forward 1.2860 | time_backward 1.4890 |
[2023-09-02 10:29:22,759::train::INFO] [train] Iter 08918 | loss 2.1043 | loss(rot) 0.1009 | loss(pos) 2.0025 | loss(seq) 0.0009 | grad 2.7414 | lr 0.0010 | time_forward 3.6600 | time_backward 5.1120 |
[2023-09-02 10:29:32,571::train::INFO] [train] Iter 08919 | loss 0.6907 | loss(rot) 0.3181 | loss(pos) 0.2853 | loss(seq) 0.0873 | grad 4.0179 | lr 0.0010 | time_forward 3.9710 | time_backward 5.8370 |
[2023-09-02 10:29:35,435::train::INFO] [train] Iter 08920 | loss 2.1622 | loss(rot) 1.9526 | loss(pos) 0.2004 | loss(seq) 0.0092 | grad 5.5817 | lr 0.0010 | time_forward 1.4340 | time_backward 1.4260 |
[2023-09-02 10:29:43,921::train::INFO] [train] Iter 08921 | loss 1.5556 | loss(rot) 0.2230 | loss(pos) 1.1108 | loss(seq) 0.2218 | grad 6.0539 | lr 0.0010 | time_forward 3.6200 | time_backward 4.8630 |
[2023-09-02 10:29:52,353::train::INFO] [train] Iter 08922 | loss 2.1151 | loss(rot) 0.0839 | loss(pos) 2.0274 | loss(seq) 0.0038 | grad 7.4480 | lr 0.0010 | time_forward 3.5130 | time_backward 4.9150 |
[2023-09-02 10:29:55,055::train::INFO] [train] Iter 08923 | loss 0.9028 | loss(rot) 0.2887 | loss(pos) 0.5136 | loss(seq) 0.1005 | grad 4.4915 | lr 0.0010 | time_forward 1.2970 | time_backward 1.4010 |
[2023-09-02 10:30:03,885::train::INFO] [train] Iter 08924 | loss 1.4921 | loss(rot) 0.8173 | loss(pos) 0.2489 | loss(seq) 0.4259 | grad 5.2677 | lr 0.0010 | time_forward 3.7300 | time_backward 5.0960 |
[2023-09-02 10:30:12,817::train::INFO] [train] Iter 08925 | loss 1.9914 | loss(rot) 1.6810 | loss(pos) 0.2706 | loss(seq) 0.0398 | grad 5.8231 | lr 0.0010 | time_forward 3.8330 | time_backward 5.0960 |
[2023-09-02 10:30:19,629::train::INFO] [train] Iter 08926 | loss 0.8713 | loss(rot) 0.1142 | loss(pos) 0.4341 | loss(seq) 0.3230 | grad 4.5961 | lr 0.0010 | time_forward 3.0050 | time_backward 3.8030 |
[2023-09-02 10:30:22,290::train::INFO] [train] Iter 08927 | loss 2.7340 | loss(rot) 2.2388 | loss(pos) 0.2526 | loss(seq) 0.2426 | grad 5.5870 | lr 0.0010 | time_forward 1.2230 | time_backward 1.4340 |
[2023-09-02 10:30:24,793::train::INFO] [train] Iter 08928 | loss 1.8595 | loss(rot) 0.9839 | loss(pos) 0.5804 | loss(seq) 0.2952 | grad 7.5007 | lr 0.0010 | time_forward 1.1950 | time_backward 1.3040 |
[2023-09-02 10:30:32,117::train::INFO] [train] Iter 08929 | loss 1.9487 | loss(rot) 1.3569 | loss(pos) 0.2402 | loss(seq) 0.3516 | grad 6.2956 | lr 0.0010 | time_forward 3.1600 | time_backward 4.1380 |
[2023-09-02 10:30:34,926::train::INFO] [train] Iter 08930 | loss 2.1849 | loss(rot) 1.3777 | loss(pos) 0.3298 | loss(seq) 0.4773 | grad 5.0897 | lr 0.0010 | time_forward 1.3230 | time_backward 1.4830 |
[2023-09-02 10:30:44,748::train::INFO] [train] Iter 08931 | loss 3.0502 | loss(rot) 0.0095 | loss(pos) 3.0401 | loss(seq) 0.0005 | grad 8.5833 | lr 0.0010 | time_forward 4.0430 | time_backward 5.7760 |
[2023-09-02 10:30:53,831::train::INFO] [train] Iter 08932 | loss 2.0151 | loss(rot) 1.3252 | loss(pos) 0.1982 | loss(seq) 0.4917 | grad 3.6027 | lr 0.0010 | time_forward 3.8980 | time_backward 5.1830 |
[2023-09-02 10:31:02,970::train::INFO] [train] Iter 08933 | loss 1.9139 | loss(rot) 1.1171 | loss(pos) 0.3839 | loss(seq) 0.4129 | grad 4.1436 | lr 0.0010 | time_forward 3.8500 | time_backward 5.2850 |
[2023-09-02 10:31:12,797::train::INFO] [train] Iter 08934 | loss 1.8257 | loss(rot) 0.9977 | loss(pos) 0.3254 | loss(seq) 0.5027 | grad 3.3833 | lr 0.0010 | time_forward 4.0860 | time_backward 5.7370 |
[2023-09-02 10:31:22,870::train::INFO] [train] Iter 08935 | loss 2.1944 | loss(rot) 1.2082 | loss(pos) 0.5126 | loss(seq) 0.4737 | grad 4.6206 | lr 0.0010 | time_forward 4.2720 | time_backward 5.7980 |
[2023-09-02 10:31:25,334::train::INFO] [train] Iter 08936 | loss 1.0469 | loss(rot) 0.1291 | loss(pos) 0.3019 | loss(seq) 0.6159 | grad 3.6211 | lr 0.0010 | time_forward 1.1650 | time_backward 1.2950 |
[2023-09-02 10:31:34,686::train::INFO] [train] Iter 08937 | loss 1.7370 | loss(rot) 1.2283 | loss(pos) 0.0921 | loss(seq) 0.4167 | grad 3.9554 | lr 0.0010 | time_forward 3.9520 | time_backward 5.3750 |
[2023-09-02 10:31:43,061::train::INFO] [train] Iter 08938 | loss 1.4014 | loss(rot) 1.1455 | loss(pos) 0.2528 | loss(seq) 0.0030 | grad 6.0032 | lr 0.0010 | time_forward 3.5560 | time_backward 4.8150 |
[2023-09-02 10:31:50,450::train::INFO] [train] Iter 08939 | loss 2.0629 | loss(rot) 1.6637 | loss(pos) 0.1502 | loss(seq) 0.2490 | grad 5.5960 | lr 0.0010 | time_forward 3.1170 | time_backward 4.2680 |
[2023-09-02 10:31:53,246::train::INFO] [train] Iter 08940 | loss 1.1142 | loss(rot) 0.4937 | loss(pos) 0.5254 | loss(seq) 0.0951 | grad 5.6289 | lr 0.0010 | time_forward 1.3090 | time_backward 1.4830 |
[2023-09-02 10:31:56,046::train::INFO] [train] Iter 08941 | loss 1.3832 | loss(rot) 0.7823 | loss(pos) 0.2078 | loss(seq) 0.3931 | grad 4.2665 | lr 0.0010 | time_forward 1.3240 | time_backward 1.4730 |
[2023-09-02 10:31:58,918::train::INFO] [train] Iter 08942 | loss 1.5668 | loss(rot) 0.8695 | loss(pos) 0.1851 | loss(seq) 0.5121 | grad 4.5756 | lr 0.0010 | time_forward 1.3810 | time_backward 1.4880 |
[2023-09-02 10:32:07,111::train::INFO] [train] Iter 08943 | loss 0.7251 | loss(rot) 0.1613 | loss(pos) 0.3963 | loss(seq) 0.1675 | grad 4.9563 | lr 0.0010 | time_forward 3.3950 | time_backward 4.7950 |
[2023-09-02 10:32:17,285::train::INFO] [train] Iter 08944 | loss 2.1599 | loss(rot) 0.4321 | loss(pos) 1.7181 | loss(seq) 0.0098 | grad 7.0429 | lr 0.0010 | time_forward 4.1000 | time_backward 6.0700 |
[2023-09-02 10:32:27,324::train::INFO] [train] Iter 08945 | loss 2.9595 | loss(rot) 2.6436 | loss(pos) 0.3158 | loss(seq) 0.0000 | grad 5.0591 | lr 0.0010 | time_forward 4.0290 | time_backward 6.0060 |
[2023-09-02 10:32:30,151::train::INFO] [train] Iter 08946 | loss 0.6458 | loss(rot) 0.2707 | loss(pos) 0.3056 | loss(seq) 0.0695 | grad 4.3358 | lr 0.0010 | time_forward 1.3630 | time_backward 1.4600 |
[2023-09-02 10:32:33,018::train::INFO] [train] Iter 08947 | loss 1.3266 | loss(rot) 0.8207 | loss(pos) 0.1116 | loss(seq) 0.3943 | grad 3.7901 | lr 0.0010 | time_forward 1.3890 | time_backward 1.4740 |
[2023-09-02 10:32:35,363::train::INFO] [train] Iter 08948 | loss 2.3460 | loss(rot) 1.9482 | loss(pos) 0.3978 | loss(seq) 0.0000 | grad 5.7402 | lr 0.0010 | time_forward 1.0990 | time_backward 1.2420 |
[2023-09-02 10:32:45,271::train::INFO] [train] Iter 08949 | loss 1.3031 | loss(rot) 0.4818 | loss(pos) 0.5193 | loss(seq) 0.3020 | grad 5.0134 | lr 0.0010 | time_forward 4.0630 | time_backward 5.8420 |
[2023-09-02 10:32:54,505::train::INFO] [train] Iter 08950 | loss 1.1360 | loss(rot) 0.3303 | loss(pos) 0.7928 | loss(seq) 0.0129 | grad 4.3362 | lr 0.0010 | time_forward 3.9500 | time_backward 5.2800 |
[2023-09-02 10:33:02,467::train::INFO] [train] Iter 08951 | loss 1.1832 | loss(rot) 0.2311 | loss(pos) 0.6902 | loss(seq) 0.2618 | grad 6.4212 | lr 0.0010 | time_forward 3.3710 | time_backward 4.5870 |
[2023-09-02 10:33:06,042::train::INFO] [train] Iter 08952 | loss 1.9936 | loss(rot) 1.6894 | loss(pos) 0.2829 | loss(seq) 0.0213 | grad 4.1974 | lr 0.0010 | time_forward 1.6300 | time_backward 1.9410 |
[2023-09-02 10:33:08,301::train::INFO] [train] Iter 08953 | loss 1.4593 | loss(rot) 0.9943 | loss(pos) 0.3704 | loss(seq) 0.0946 | grad 6.5212 | lr 0.0010 | time_forward 1.0460 | time_backward 1.2110 |
[2023-09-02 10:33:11,044::train::INFO] [train] Iter 08954 | loss 3.2476 | loss(rot) 2.9515 | loss(pos) 0.2955 | loss(seq) 0.0006 | grad 5.6249 | lr 0.0010 | time_forward 1.2710 | time_backward 1.4680 |
[2023-09-02 10:33:14,659::train::INFO] [train] Iter 08955 | loss 2.4212 | loss(rot) 1.9786 | loss(pos) 0.2512 | loss(seq) 0.1914 | grad 3.3595 | lr 0.0010 | time_forward 1.7040 | time_backward 1.9070 |
[2023-09-02 10:33:24,941::train::INFO] [train] Iter 08956 | loss 2.4041 | loss(rot) 2.2786 | loss(pos) 0.1118 | loss(seq) 0.0138 | grad 3.8631 | lr 0.0010 | time_forward 4.1690 | time_backward 6.1100 |
[2023-09-02 10:33:33,739::train::INFO] [train] Iter 08957 | loss 1.7284 | loss(rot) 0.8184 | loss(pos) 0.5294 | loss(seq) 0.3807 | grad 7.0982 | lr 0.0010 | time_forward 3.6940 | time_backward 5.1000 |
[2023-09-02 10:33:43,800::train::INFO] [train] Iter 08958 | loss 2.4868 | loss(rot) 1.7253 | loss(pos) 0.3129 | loss(seq) 0.4487 | grad 4.3173 | lr 0.0010 | time_forward 4.2280 | time_backward 5.8300 |
[2023-09-02 10:33:53,921::train::INFO] [train] Iter 08959 | loss 1.6114 | loss(rot) 1.4239 | loss(pos) 0.0928 | loss(seq) 0.0946 | grad 3.4823 | lr 0.0010 | time_forward 4.2780 | time_backward 5.8400 |
[2023-09-02 10:33:58,838::train::INFO] [train] Iter 08960 | loss 2.4953 | loss(rot) 1.4809 | loss(pos) 0.4440 | loss(seq) 0.5703 | grad 6.5762 | lr 0.0010 | time_forward 2.1160 | time_backward 2.7980 |
[2023-09-02 10:34:08,659::train::INFO] [train] Iter 08961 | loss 1.6231 | loss(rot) 0.4356 | loss(pos) 0.6471 | loss(seq) 0.5404 | grad 5.0613 | lr 0.0010 | time_forward 3.9060 | time_backward 5.9110 |
[2023-09-02 10:34:12,081::train::INFO] [train] Iter 08962 | loss 1.5848 | loss(rot) 0.2156 | loss(pos) 1.0581 | loss(seq) 0.3112 | grad 4.3879 | lr 0.0010 | time_forward 1.5460 | time_backward 1.8740 |
[2023-09-02 10:34:20,491::train::INFO] [train] Iter 08963 | loss 1.1165 | loss(rot) 1.0102 | loss(pos) 0.0990 | loss(seq) 0.0073 | grad 5.7783 | lr 0.0010 | time_forward 3.5320 | time_backward 4.8740 |
[2023-09-02 10:34:23,303::train::INFO] [train] Iter 08964 | loss 1.3610 | loss(rot) 0.1024 | loss(pos) 1.2561 | loss(seq) 0.0026 | grad 5.9029 | lr 0.0010 | time_forward 1.3040 | time_backward 1.5040 |
[2023-09-02 10:34:32,620::train::INFO] [train] Iter 08965 | loss 1.2600 | loss(rot) 0.7251 | loss(pos) 0.1433 | loss(seq) 0.3916 | grad 4.9133 | lr 0.0010 | time_forward 3.9590 | time_backward 5.3560 |
[2023-09-02 10:34:41,901::train::INFO] [train] Iter 08966 | loss 1.8294 | loss(rot) 1.3497 | loss(pos) 0.2019 | loss(seq) 0.2778 | grad 5.5031 | lr 0.0010 | time_forward 3.9190 | time_backward 5.3570 |
[2023-09-02 10:34:52,141::train::INFO] [train] Iter 08967 | loss 2.1939 | loss(rot) 1.6303 | loss(pos) 0.1947 | loss(seq) 0.3689 | grad 3.6709 | lr 0.0010 | time_forward 4.2760 | time_backward 5.9600 |
[2023-09-02 10:35:00,585::train::INFO] [train] Iter 08968 | loss 2.3801 | loss(rot) 2.1423 | loss(pos) 0.1913 | loss(seq) 0.0465 | grad 4.4471 | lr 0.0010 | time_forward 3.5950 | time_backward 4.8450 |
[2023-09-02 10:35:08,964::train::INFO] [train] Iter 08969 | loss 2.2237 | loss(rot) 2.0216 | loss(pos) 0.1817 | loss(seq) 0.0204 | grad 4.3216 | lr 0.0010 | time_forward 3.6130 | time_backward 4.7630 |
[2023-09-02 10:35:18,588::train::INFO] [train] Iter 08970 | loss 1.8767 | loss(rot) 1.3010 | loss(pos) 0.1499 | loss(seq) 0.4259 | grad 4.3272 | lr 0.0010 | time_forward 4.2520 | time_backward 5.3690 |
[2023-09-02 10:35:27,848::train::INFO] [train] Iter 08971 | loss 2.5090 | loss(rot) 2.2396 | loss(pos) 0.2437 | loss(seq) 0.0257 | grad 3.5226 | lr 0.0010 | time_forward 3.8240 | time_backward 5.4320 |
[2023-09-02 10:35:36,197::train::INFO] [train] Iter 08972 | loss 1.0678 | loss(rot) 0.2763 | loss(pos) 0.6639 | loss(seq) 0.1276 | grad 3.8396 | lr 0.0010 | time_forward 3.5430 | time_backward 4.8020 |
[2023-09-02 10:35:46,318::train::INFO] [train] Iter 08973 | loss 2.5720 | loss(rot) 1.5074 | loss(pos) 0.4766 | loss(seq) 0.5880 | grad 4.9207 | lr 0.0010 | time_forward 4.2560 | time_backward 5.8610 |
[2023-09-02 10:35:49,077::train::INFO] [train] Iter 08974 | loss 0.7063 | loss(rot) 0.0440 | loss(pos) 0.6571 | loss(seq) 0.0052 | grad 5.5381 | lr 0.0010 | time_forward 1.3070 | time_backward 1.4490 |
[2023-09-02 10:36:00,491::train::INFO] [train] Iter 08975 | loss 2.1345 | loss(rot) 1.4771 | loss(pos) 0.1814 | loss(seq) 0.4761 | grad 5.8166 | lr 0.0010 | time_forward 5.4600 | time_backward 5.9240 |
[2023-09-02 10:36:03,235::train::INFO] [train] Iter 08976 | loss 1.6862 | loss(rot) 0.8368 | loss(pos) 0.2078 | loss(seq) 0.6417 | grad 3.4817 | lr 0.0010 | time_forward 1.2510 | time_backward 1.4890 |
[2023-09-02 10:36:07,243::train::INFO] [train] Iter 08977 | loss 1.9578 | loss(rot) 0.1529 | loss(pos) 1.8028 | loss(seq) 0.0021 | grad 3.2199 | lr 0.0010 | time_forward 1.9740 | time_backward 2.0300 |
[2023-09-02 10:36:15,991::train::INFO] [train] Iter 08978 | loss 0.8048 | loss(rot) 0.0736 | loss(pos) 0.7066 | loss(seq) 0.0246 | grad 3.4096 | lr 0.0010 | time_forward 3.9500 | time_backward 4.7950 |
[2023-09-02 10:36:18,682::train::INFO] [train] Iter 08979 | loss 1.5105 | loss(rot) 0.2783 | loss(pos) 1.2213 | loss(seq) 0.0110 | grad 5.6686 | lr 0.0010 | time_forward 1.2330 | time_backward 1.4540 |
[2023-09-02 10:36:28,568::train::INFO] [train] Iter 08980 | loss 1.7803 | loss(rot) 1.5582 | loss(pos) 0.2218 | loss(seq) 0.0003 | grad 4.6216 | lr 0.0010 | time_forward 4.1550 | time_backward 5.7280 |
[2023-09-02 10:36:36,692::train::INFO] [train] Iter 08981 | loss 1.7249 | loss(rot) 1.5403 | loss(pos) 0.1726 | loss(seq) 0.0120 | grad 5.0302 | lr 0.0010 | time_forward 3.3620 | time_backward 4.7590 |
[2023-09-02 10:36:39,368::train::INFO] [train] Iter 08982 | loss 1.3461 | loss(rot) 1.2390 | loss(pos) 0.1070 | loss(seq) 0.0000 | grad 4.5260 | lr 0.0010 | time_forward 1.2440 | time_backward 1.4280 |
[2023-09-02 10:36:48,185::train::INFO] [train] Iter 08983 | loss 1.8082 | loss(rot) 0.0093 | loss(pos) 1.7984 | loss(seq) 0.0005 | grad 5.4970 | lr 0.0010 | time_forward 3.7330 | time_backward 5.0810 |
[2023-09-02 10:36:56,315::train::INFO] [train] Iter 08984 | loss 2.0534 | loss(rot) 1.2439 | loss(pos) 0.1571 | loss(seq) 0.6525 | grad 6.8726 | lr 0.0010 | time_forward 3.4280 | time_backward 4.6980 |
[2023-09-02 10:37:05,421::train::INFO] [train] Iter 08985 | loss 1.9526 | loss(rot) 1.2363 | loss(pos) 0.2794 | loss(seq) 0.4369 | grad 5.1387 | lr 0.0010 | time_forward 3.8700 | time_backward 5.2320 |
[2023-09-02 10:37:14,154::train::INFO] [train] Iter 08986 | loss 1.0227 | loss(rot) 0.4619 | loss(pos) 0.2457 | loss(seq) 0.3152 | grad 3.4764 | lr 0.0010 | time_forward 3.6850 | time_backward 5.0450 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.