MST 训练

Question

MST 训练

xcyquan opened this issue 2 years ago · comments

尊敬的作者您好，
python train.py --method mst --batch_size 20 --end_epoch 300 --init_lr 4e-4 --outf ./exp/mst/ --data_root ../dataset/ --patch_size 128 --stride 8 --gpu_id 0
使用上面的命令运行，得到的结果是这样的，别的模型是可以正常跑通的，只有这个mst，是我哪里弄错了吗？希望能得到您的解答！
2022-04-23 09:47:00 - Iter[001000], Epoch[000001], learning rate : 0.000399989, Train Loss: 0.646447778, Test MRAE: 0.531796575, Test RMSE: 0.091432519, Test PSNR: 19.090953827
2022-04-23 10:02:48 - Iter[002000], Epoch[000002], learning rate : 0.000399956, Train Loss: 0.578481972, Test MRAE: 0.491525143, Test RMSE: 0.086475834, Test PSNR: 19.247301102
2022-04-23 10:18:36 - Iter[003000], Epoch[000003], learning rate : 0.000399902, Train Loss: 0.547274351, Test MRAE: 0.494777530, Test RMSE: 0.079640247, Test PSNR: 19.196660995
2022-04-23 10:34:24 - Iter[004000], Epoch[000004], learning rate : 0.000399825, Train Loss: 0.526349247, Test MRAE: 0.472677380, Test RMSE: 0.069328219, Test PSNR: 19.027240753
2022-04-23 10:50:12 - Iter[005000], Epoch[000005], learning rate : 0.000399727, Train Loss: 0.511086941, Test MRAE: 0.358050972, Test RMSE: 0.059412364, Test PSNR: 19.215837479
2022-04-23 11:06:00 - Iter[006000], Epoch[000006], learning rate : 0.000399606, Train Loss: 0.499529332, Test MRAE: 0.356865495, Test RMSE: 0.055258289, Test PSNR: 19.074251175
2022-04-23 11:21:49 - Iter[007000], Epoch[000007], learning rate : 0.000399464, Train Loss: 0.489558429, Test MRAE: 0.378400117, Test RMSE: 0.057141136, Test PSNR: 19.100608826
2022-04-23 11:37:37 - Iter[008000], Epoch[000008], learning rate : 0.000399301, Train Loss: 0.481531292, Test MRAE: 0.362446398, Test RMSE: 0.056064066, Test PSNR: 19.177246094
2022-04-23 11:53:25 - Iter[009000], Epoch[000009], learning rate : 0.000399115, Train Loss: 0.474746048, Test MRAE: 0.331809163, Test RMSE: 0.051642809, Test PSNR: 19.225530624
2022-04-23 12:09:13 - Iter[010000], Epoch[000010], learning rate : 0.000398907, Train Loss: 0.468800724, Test MRAE: 0.300195664, Test RMSE: 0.046801656, Test PSNR: 19.080495834
2022-04-23 12:25:01 - Iter[011000], Epoch[000011], learning rate : 0.000398678, Train Loss: 0.463295877, Test MRAE: 0.327608109, Test RMSE: 0.050521433, Test PSNR: 19.182096481
2022-04-23 12:40:49 - Iter[012000], Epoch[000012], learning rate : 0.000398427, Train Loss: 0.458517045, Test MRAE: 0.422704875, Test RMSE: 0.066085078, Test PSNR: 19.202255249
2022-04-23 12:56:38 - Iter[013000], Epoch[000013], learning rate : 0.000398154, Train Loss: 0.453951567, Test MRAE: 0.444770008, Test RMSE: 0.066878960, Test PSNR: 18.973522186
2022-04-23 13:12:27 - Iter[014000], Epoch[000014], learning rate : 0.000397860, Train Loss: 0.447406828, Test MRAE: 0.336395442, Test RMSE: 0.050711267, Test PSNR: 19.189912796
2022-04-23 13:28:16 - Iter[015000], Epoch[000015], learning rate : 0.000397544, Train Loss: 0.440287501, Test MRAE: 0.364379525, Test RMSE: 0.054785427, Test PSNR: 19.161180496
2022-04-23 13:44:06 - Iter[016000], Epoch[000016], learning rate : 0.000397207, Train Loss: 0.432897270, Test MRAE: 0.370432645, Test RMSE: 0.056947071, Test PSNR: 19.268909454
2022-04-23 13:59:54 - Iter[017000], Epoch[000017], learning rate : 0.000396847, Train Loss: 0.425944477, Test MRAE: 0.386497170, Test RMSE: 0.057578120, Test PSNR: 19.306638718
2022-04-23 14:15:41 - Iter[018000], Epoch[000018], learning rate : 0.000396467, Train Loss: 0.419327229, Test MRAE: 0.381819218, Test RMSE: 0.054971345, Test PSNR: 19.137437820
2022-04-23 14:31:30 - Iter[019000], Epoch[000019], learning rate : 0.000396065, Train Loss: 0.412961543, Test MRAE: 0.268086284, Test RMSE: 0.039695993, Test PSNR: 19.237449646
2022-04-23 14:47:18 - Iter[020000], Epoch[000020], learning rate : 0.000395641, Train Loss: 0.406925291, Test MRAE: 0.278270304, Test RMSE: 0.040124148, Test PSNR: 19.128618240
2022-04-23 15:03:06 - Iter[021000], Epoch[000021], learning rate : 0.000395196, Train Loss: 0.401020914, Test MRAE: 0.263858318, Test RMSE: 0.039533190, Test PSNR: 19.288784027
2022-04-23 15:18:55 - Iter[022000], Epoch[000022], learning rate : 0.000394729, Train Loss: 0.395406336, Test MRAE: 0.293986678, Test RMSE: 0.042647284, Test PSNR: 19.238164902
2022-04-23 15:34:43 - Iter[023000], Epoch[000023], learning rate : 0.000394242, Train Loss: 0.390212119, Test MRAE: 0.291280866, Test RMSE: 0.042363208, Test PSNR: 19.284023285
2022-04-23 15:50:32 - Iter[024000], Epoch[000024], learning rate : 0.000393733, Train Loss: 0.385213077, Test MRAE: 0.233923718, Test RMSE: 0.036982559, Test PSNR: 19.216468811
2022-04-23 16:06:20 - Iter[025000], Epoch[000025], learning rate : 0.000393203, Train Loss: 0.380559415, Test MRAE: 0.260465741, Test RMSE: 0.039078295, Test PSNR: 19.235170364
2022-04-23 16:22:09 - Iter[026000], Epoch[000026], learning rate : 0.000392651, Train Loss: 0.375919461, Test MRAE: 0.254196465, Test RMSE: 0.036160678, Test PSNR: 19.114507675
2022-04-23 16:37:58 - Iter[027000], Epoch[000027], learning rate : 0.000392079, Train Loss: 0.371591926, Test MRAE: 0.314058006, Test RMSE: 0.044734038, Test PSNR: 19.360179901
2022-04-23 16:53:49 - Iter[028000], Epoch[000028], learning rate : 0.000391486, Train Loss: 0.367457062, Test MRAE: 0.226818457, Test RMSE: 0.031798869, Test PSNR: 18.716556549
2022-04-23 17:09:37 - Iter[029000], Epoch[000029], learning rate : 0.000390872, Train Loss: 0.363370985, Test MRAE: 0.271478027, Test RMSE: 0.041398086, Test PSNR: 19.368923187
2022-04-23 17:25:25 - Iter[030000], Epoch[000030], learning rate : 0.000390236, Train Loss: 0.359518796, Test MRAE: 0.293181419, Test RMSE: 0.040424421, Test PSNR: 19.312316895
2022-04-23 17:41:13 - Iter[031000], Epoch[000031], learning rate : 0.000389580, Train Loss: 0.355821848, Test MRAE: 0.245824501, Test RMSE: 0.034910016, Test PSNR: 19.084083557
2022-04-23 17:57:02 - Iter[032000], Epoch[000032], learning rate : 0.000388904, Train Loss: 0.352144718, Test MRAE: 0.266790211, Test RMSE: 0.040394772, Test PSNR: 19.260843277
2022-04-23 18:12:53 - Iter[033000], Epoch[000033], learning rate : 0.000388206, Train Loss: 0.348656774, Test MRAE: 0.222802311, Test RMSE: 0.034814272, Test PSNR: 19.181829453
2022-04-23 18:28:42 - Iter[034000], Epoch[000034], learning rate : 0.000387488, Train Loss: 0.345295727, Test MRAE: 0.298925221, Test RMSE: 0.043574888, Test PSNR: 19.372837067
2022-04-23 18:44:32 - Iter[035000], Epoch[000035], learning rate : 0.000386750, Train Loss: 0.342086405, Test MRAE: 0.234956443, Test RMSE: 0.036083721, Test PSNR: 19.267917633
2022-04-23 19:00:22 - Iter[036000], Epoch[000036], learning rate : 0.000385991, Train Loss: 0.338931412, Test MRAE: 0.228018716, Test RMSE: 0.032620184, Test PSNR: 19.047657013
2022-04-23 19:16:11 - Iter[037000], Epoch[000037], learning rate : 0.000385212, Train Loss: 0.335954189, Test MRAE: 0.229232669, Test RMSE: 0.034972440, Test PSNR: 19.219429016
2022-04-23 19:31:59 - Iter[038000], Epoch[000038], learning rate : 0.000384413, Train Loss: 0.333125830, Test MRAE: 0.230264142, Test RMSE: 0.034406129, Test PSNR: 19.242214203
2022-04-23 19:47:48 - Iter[039000], Epoch[000039], learning rate : 0.000383593, Train Loss: 0.330276132, Test MRAE: 0.274099082, Test RMSE: 0.043192532, Test PSNR: 19.348083496
2022-04-23 20:03:35 - Iter[040000], Epoch[000040], learning rate : 0.000382753, Train Loss: 0.327579051, Test MRAE: 0.218261853, Test RMSE: 0.031082967, Test PSNR: 18.932905197
2022-04-23 20:19:24 - Iter[041000], Epoch[000041], learning rate : 0.000381893, Train Loss: 0.324890018, Test MRAE: 0.254400879, Test RMSE: 0.038573589, Test PSNR: 19.314989090
2022-04-23 20:35:13 - Iter[042000], Epoch[000042], learning rate : 0.000381014, Train Loss: 0.322247148, Test MRAE: 0.259322703, Test RMSE: 0.038424168, Test PSNR: 19.213300705
2022-04-23 20:51:02 - Iter[043000], Epoch[000043], learning rate : 0.000380115, Train Loss: 0.319739074, Test MRAE: 0.238067225, Test RMSE: 0.035432223, Test PSNR: 19.308258057
2022-04-23 21:06:51 - Iter[044000], Epoch[000044], learning rate : 0.000379195, Train Loss: 0.317296684, Test MRAE: 0.216048419, Test RMSE: 0.031597815, Test PSNR: 19.090858459
2022-04-23 21:22:38 - Iter[045000], Epoch[000045], learning rate : 0.000378257, Train Loss: 0.314926445, Test MRAE: 0.235811070, Test RMSE: 0.034594744, Test PSNR: 19.259517670
2022-04-23 21:38:27 - Iter[046000], Epoch[000046], learning rate : 0.000377299, Train Loss: 0.312630028, Test MRAE: 0.209554464, Test RMSE: 0.030831426, Test PSNR: 19.153333664
2022-04-23 21:54:16 - Iter[047000], Epoch[000047], learning rate : 0.000376321, Train Loss: 0.310319424, Test MRAE: 0.214102373, Test RMSE: 0.031406451, Test PSNR: 19.192979813
2022-04-23 22:10:04 - Iter[048000], Epoch[000048], learning rate : 0.000375324, Train Loss: 0.308094501, Test MRAE: 0.199715927, Test RMSE: 0.030652732, Test PSNR: 19.189508438
2022-04-23 22:25:51 - Iter[049000], Epoch[000049], learning rate : 0.000374308, Train Loss: 0.305966139, Test MRAE: 0.218578279, Test RMSE: 0.031519547, Test PSNR: 19.050167084
2022-04-23 22:41:40 - Iter[050000], Epoch[000050], learning rate : 0.000373273, Train Loss: 0.303862125, Test MRAE: 0.215297714, Test RMSE: 0.032994971, Test PSNR: 19.179225922
2022-04-23 22:57:28 - Iter[051000], Epoch[000051], learning rate : 0.000372219, Train Loss: 0.301793545, Test MRAE: 0.231257230, Test RMSE: 0.032595847, Test PSNR: 19.175262451
2022-04-23 23:13:17 - Iter[052000], Epoch[000052], learning rate : 0.000371146, Train Loss: 0.299778074, Test MRAE: 0.253783792, Test RMSE: 0.037507851, Test PSNR: 19.363800049
2022-04-23 23:29:05 - Iter[053000], Epoch[000053], learning rate : 0.000370055, Train Loss: 0.297876358, Test MRAE: 0.258977175, Test RMSE: 0.040128287, Test PSNR: 19.314584732
2022-04-23 23:44:53 - Iter[054000], Epoch[000054], learning rate : 0.000368945, Train Loss: 0.295973837, Test MRAE: 0.217594683, Test RMSE: 0.032133736, Test PSNR: 19.153348923
2022-04-24 00:00:41 - Iter[055000], Epoch[000055], learning rate : 0.000367816, Train Loss: 0.294095606, Test MRAE: 0.216711819, Test RMSE: 0.031819437, Test PSNR: 19.180938721
2022-04-24 00:16:29 - Iter[056000], Epoch[000056], learning rate : 0.000366669, Train Loss: 0.292232960, Test MRAE: 0.207258597, Test RMSE: 0.029869573, Test PSNR: 19.150335312
2022-04-24 00:32:17 - Iter[057000], Epoch[000057], learning rate : 0.000365504, Train Loss: 0.290452927, Test MRAE: 0.198026657, Test RMSE: 0.028679363, Test PSNR: 19.005662918
2022-04-24 00:48:05 - Iter[058000], Epoch[000058], learning rate : 0.000364320, Train Loss: 0.288685858, Test MRAE: 0.221201345, Test RMSE: 0.033238016, Test PSNR: 19.217224121
2022-04-24 01:03:54 - Iter[059000], Epoch[000059], learning rate : 0.000363119, Train Loss: 0.287011743, Test MRAE: 0.213097245, Test RMSE: 0.032143150, Test PSNR: 19.209981918
2022-04-24 01:19:42 - Iter[060000], Epoch[000060], learning rate : 0.000361900, Train Loss: 0.285367638, Test MRAE: 0.193839341, Test RMSE: 0.027465345, Test PSNR: 18.971504211
2022-04-24 01:35:30 - Iter[061000], Epoch[000061], learning rate : 0.000360663, Train Loss: 0.283741534, Test MRAE: 0.196817130, Test RMSE: 0.029001579, Test PSNR: 19.062067032
2022-04-24 01:51:19 - Iter[062000], Epoch[000062], learning rate : 0.000359409, Train Loss: 0.282072276, Test MRAE: 0.219744995, Test RMSE: 0.029378273, Test PSNR: 18.866895676
2022-04-24 02:07:07 - Iter[063000], Epoch[000063], learning rate : 0.000358137, Train Loss: 0.280501366, Test MRAE: 0.229305848, Test RMSE: 0.033509906, Test PSNR: 19.214229584
2022-04-24 02:22:55 - Iter[064000], Epoch[000064], learning rate : 0.000356848, Train Loss: 0.278931111, Test MRAE: 0.206814021, Test RMSE: 0.030476322, Test PSNR: 19.040904999
2022-04-24 02:38:42 - Iter[065000], Epoch[000065], learning rate : 0.000355542, Train Loss: 0.277395368, Test MRAE: 0.208180115, Test RMSE: 0.031854365, Test PSNR: 19.156114578
2022-04-24 02:54:30 - Iter[066000], Epoch[000066], learning rate : 0.000354219, Train Loss: 0.275906801, Test MRAE: 0.195947483, Test RMSE: 0.030340478, Test PSNR: 19.152935028
2022-04-24 03:10:18 - Iter[067000], Epoch[000067], learning rate : 0.000352879, Train Loss: 0.274478436, Test MRAE: 0.220566273, Test RMSE: 0.032208432, Test PSNR: 18.987400055
2022-04-24 03:26:06 - Iter[068000], Epoch[000068], learning rate : 0.000351522, Train Loss: 0.273031890, Test MRAE: 0.198420197, Test RMSE: 0.029046385, Test PSNR: 18.940135956
2022-04-24 03:41:54 - Iter[069000], Epoch[000069], learning rate : 0.000350149, Train Loss: 0.271648794, Test MRAE: 0.240019321, Test RMSE: 0.034521896, Test PSNR: 19.117261887
2022-04-24 03:57:42 - Iter[070000], Epoch[000070], learning rate : 0.000348759, Train Loss: 0.270240217, Test MRAE: 0.203085661, Test RMSE: 0.028956201, Test PSNR: 19.012472153
2022-04-24 04:13:31 - Iter[071000], Epoch[000071], learning rate : 0.000347353, Train Loss: 0.268938541, Test MRAE: 0.213608027, Test RMSE: 0.031390432, Test PSNR: 18.951906204
2022-04-24 04:29:19 - Iter[072000], Epoch[000072], learning rate : 0.000345931, Train Loss: 0.267599642, Test MRAE: 0.231374159, Test RMSE: 0.031735256, Test PSNR: 19.098361969
2022-04-24 04:45:06 - Iter[073000], Epoch[000073], learning rate : 0.000344493, Train Loss: 0.266298503, Test MRAE: 0.250184625, Test RMSE: 0.035862882, Test PSNR: 19.221879959
2022-04-24 05:00:54 - Iter[074000], Epoch[000074], learning rate : 0.000343039, Train Loss: 0.264970332, Test MRAE: 0.216034293, Test RMSE: 0.032297533, Test PSNR: 19.119438171
2022-04-24 05:16:41 - Iter[075000], Epoch[000075], learning rate : 0.000341569, Train Loss: 0.263698131, Test MRAE: 0.236452579, Test RMSE: 0.034641251, Test PSNR: 19.267896652
2022-04-24 05:32:29 - Iter[076000], Epoch[000076], learning rate : 0.000340084, Train Loss: 0.262492269, Test MRAE: 0.226200759, Test RMSE: 0.032904580, Test PSNR: 19.063945770
2022-04-24 05:48:17 - Iter[077000], Epoch[000077], learning rate : 0.000338584, Train Loss: 0.261239469, Test MRAE: 0.201683655, Test RMSE: 0.029232167, Test PSNR: 18.907796860
2022-04-24 06:04:04 - Iter[078000], Epoch[000078], learning rate : 0.000337069, Train Loss: 0.260039657, Test MRAE: 0.214427084, Test RMSE: 0.031692069, Test PSNR: 18.943357468
2022-04-24 06:19:52 - Iter[079000], Epoch[000079], learning rate : 0.000335538, Train Loss: 0.258839667, Test MRAE: 0.209781334, Test RMSE: 0.030609982, Test PSNR: 19.051986694
2022-04-24 06:35:40 - Iter[080000], Epoch[000080], learning rate : 0.000333993, Train Loss: 0.257642329, Test MRAE: 0.192540377, Test RMSE: 0.029445488, Test PSNR: 19.034675598
2022-04-24 06:51:25 - Iter[081000], Epoch[000081], learning rate : 0.000332433, Train Loss: 0.256517678, Test MRAE: 0.222092792, Test RMSE: 0.030401962, Test PSNR: 18.930913925
2022-04-24 07:07:08 - Iter[082000], Epoch[000082], learning rate : 0.000330859, Train Loss: 0.255394250, Test MRAE: 0.209056050, Test RMSE: 0.029050352, Test PSNR: 19.042034149
2022-04-24 07:22:51 - Iter[083000], Epoch[000083], learning rate : 0.000329270, Train Loss: 0.254281640, Test MRAE: 0.214589760, Test RMSE: 0.030067844, Test PSNR: 18.710792542
2022-04-24 07:38:34 - Iter[084000], Epoch[000084], learning rate : 0.000327668, Train Loss: 0.253181159, Test MRAE: 0.198045820, Test RMSE: 0.028749663, Test PSNR: 19.026756287
2022-04-24 07:54:19 - Iter[085000], Epoch[000085], learning rate : 0.000326051, Train Loss: 0.252079219, Test MRAE: 0.212200597, Test RMSE: 0.030908102, Test PSNR: 19.004436493
2022-04-24 08:10:03 - Iter[086000], Epoch[000086], learning rate : 0.000324421, Train Loss: 0.251013666, Test MRAE: 0.205034807, Test RMSE: 0.029491324, Test PSNR: 19.155452728
2022-04-24 08:25:51 - Iter[087000], Epoch[000087], learning rate : 0.000322777, Train Loss: 0.249932632, Test MRAE: 0.192795992, Test RMSE: 0.028440528, Test PSNR: 19.070756912
2022-04-24 08:41:33 - Iter[088000], Epoch[000088], learning rate : 0.000321119, Train Loss: 0.248863876, Test MRAE: 0.206967190, Test RMSE: 0.028357433, Test PSNR: 18.958072662
2022-04-24 08:57:17 - Iter[089000], Epoch[000089], learning rate : 0.000319449, Train Loss: 0.247781381, Test MRAE: 0.212967843, Test RMSE: 0.029828170, Test PSNR: 18.949298859
2022-04-24 09:12:59 - Iter[090000], Epoch[000090], learning rate : 0.000317765, Train Loss: 0.246814713, Test MRAE: 0.217871219, Test RMSE: 0.031028254, Test PSNR: 19.021213531
2022-04-24 09:28:41 - Iter[091000], Epoch[000091], learning rate : 0.000316068, Train Loss: 0.245769218, Test MRAE: 0.222220868, Test RMSE: 0.031629700, Test PSNR: 19.008470535
2022-04-24 09:44:25 - Iter[092000], Epoch[000092], learning rate : 0.000314359, Train Loss: 0.244765893, Test MRAE: 0.201765835, Test RMSE: 0.030243544, Test PSNR: 19.158527374
2022-04-24 10:00:07 - Iter[093000], Epoch[000093], learning rate : 0.000312637, Train Loss: 0.243786022, Test MRAE: 0.238795847, Test RMSE: 0.035489723, Test PSNR: 19.237535477
2022-04-24 10:15:52 - Iter[094000], Epoch[000094], learning rate : 0.000310903, Train Loss: 0.242815033, Test MRAE: 0.227278069, Test RMSE: 0.032577001, Test PSNR: 19.165273666
2022-04-24 10:31:36 - Iter[095000], Epoch[000095], learning rate : 0.000309157, Train Loss: 0.241861641, Test MRAE: 0.206680715, Test RMSE: 0.029225241, Test PSNR: 19.057720184
2022-04-24 10:47:19 - Iter[096000], Epoch[000096], learning rate : 0.000307399, Train Loss: 0.240916863, Test MRAE: 0.221876547, Test RMSE: 0.031988315, Test PSNR: 19.071466446
2022-04-24 11:03:01 - Iter[097000], Epoch[000097], learning rate : 0.000305629, Train Loss: 0.239972606, Test MRAE: 0.205407277, Test RMSE: 0.029455291, Test PSNR: 18.912952423
2022-04-24 11:18:43 - Iter[098000], Epoch[000098], learning rate : 0.000303848, Train Loss: 0.239050597, Test MRAE: 0.218140364, Test RMSE: 0.031495962, Test PSNR: 19.028364182
2022-04-24 11:34:26 - Iter[099000], Epoch[000099], learning rate : 0.000302056, Train Loss: 0.145563006, Test MRAE: 0.212117374, Test RMSE: 0.029292498, Test PSNR: 18.875003815
2022-04-24 11:50:08 - Iter[100000], Epoch[000100], learning rate : 0.000300252, Train Loss: 0.145217299, Test MRAE: 0.214958370, Test RMSE: 0.030232767, Test PSNR: 18.982784271
2022-04-24 12:05:50 - Iter[101000], Epoch[000101], learning rate : 0.000298437, Train Loss: 0.146453694, Test MRAE: 0.227019936, Test RMSE: 0.032480333, Test PSNR: 19.083282471
2022-04-24 12:21:38 - Iter[102000], Epoch[000102], learning rate : 0.000296612, Train Loss: 0.146322653, Test MRAE: 0.239566207, Test RMSE: 0.035496548, Test PSNR: 19.104885101
2022-04-24 12:37:20 - Iter[103000], Epoch[000103], learning rate : 0.000294776, Train Loss: 0.146285564, Test MRAE: 0.191646069, Test RMSE: 0.028251326, Test PSNR: 19.006746292
2022-04-24 12:53:03 - Iter[104000], Epoch[000104], learning rate : 0.000292929, Train Loss: 0.146255314, Test MRAE: 0.234831825, Test RMSE: 0.034441300, Test PSNR: 19.204271317
2022-04-24 13:08:45 - Iter[105000], Epoch[000105], learning rate : 0.000291073, Train Loss: 0.145497769, Test MRAE: 0.207711518, Test RMSE: 0.030052185, Test PSNR: 19.077249527
2022-04-24 13:24:27 - Iter[106000], Epoch[000106], learning rate : 0.000289207, Train Loss: 0.145523682, Test MRAE: 0.222439647, Test RMSE: 0.032102700, Test PSNR: 19.164697647
2022-04-24 13:40:12 - Iter[107000], Epoch[000107], learning rate : 0.000287330, Train Loss: 0.145412102, Test MRAE: 0.200026944, Test RMSE: 0.027787490, Test PSNR: 18.735790253
2022-04-24 13:56:02 - Iter[108000], Epoch[000108], learning rate : 0.000285445, Train Loss: 0.145686775, Test MRAE: 0.211092830, Test RMSE: 0.031725895, Test PSNR: 19.057603836
2022-04-24 14:11:52 - Iter[109000], Epoch[000109], learning rate : 0.000283550, Train Loss: 0.145138130, Test MRAE: 0.202462837, Test RMSE: 0.029094383, Test PSNR: 19.105033875
2022-04-24 14:27:39 - Iter[110000], Epoch[000110], learning rate : 0.000281646, Train Loss: 0.144788370, Test MRAE: 0.204958782, Test RMSE: 0.028470399, Test PSNR: 19.004680634
2022-04-24 14:43:25 - Iter[111000], Epoch[000111], learning rate : 0.000279733, Train Loss: 0.144440651, Test MRAE: 0.194457725, Test RMSE: 0.028255261, Test PSNR: 18.978731155
2022-04-24 14:59:13 - Iter[112000], Epoch[000112], learning rate : 0.000277811, Train Loss: 0.144009396, Test MRAE: 0.197285131, Test RMSE: 0.028200772, Test PSNR: 18.996498108
2022-04-24 15:15:00 - Iter[113000], Epoch[000113], learning rate : 0.000275881, Train Loss: 0.143576682, Test MRAE: 0.203258514, Test RMSE: 0.029270183, Test PSNR: 18.912971497
2022-04-24 15:30:45 - Iter[114000], Epoch[000114], learning rate : 0.000273943, Train Loss: 0.143229470, Test MRAE: 0.219165146, Test RMSE: 0.031687632, Test PSNR: 19.071453094
2022-04-24 15:46:33 - Iter[115000], Epoch[000115], learning rate : 0.000271996, Train Loss: 0.142740473, Test MRAE: 0.216698647, Test RMSE: 0.031449940, Test PSNR: 19.045347214
2022-04-24 16:02:23 - Iter[116000], Epoch[000116], learning rate : 0.000270042, Train Loss: 0.142507568, Test MRAE: 0.193366423, Test RMSE: 0.028129779, Test PSNR: 19.005863190
2022-04-24 16:18:10 - Iter[117000], Epoch[000117], learning rate : 0.000268080, Train Loss: 0.142188013, Test MRAE: 0.219309017, Test RMSE: 0.030824291, Test PSNR: 19.017368317
2022-04-24 16:33:56 - Iter[118000], Epoch[000118], learning rate : 0.000266111, Train Loss: 0.141825795, Test MRAE: 0.205177620, Test RMSE: 0.028586203, Test PSNR: 18.947324753
2022-04-24 16:49:44 - Iter[119000], Epoch[000119], learning rate : 0.000264134, Train Loss: 0.141454026, Test MRAE: 0.213753939, Test RMSE: 0.030987954, Test PSNR: 19.093702316
2022-04-24 17:05:34 - Iter[120000], Epoch[000120], learning rate : 0.000262151, Train Loss: 0.141122550, Test MRAE: 0.205288440, Test RMSE: 0.029451758, Test PSNR: 19.051704407
2022-04-24 17:21:21 - Iter[121000], Epoch[000121], learning rate : 0.000260161, Train Loss: 0.141675979, Test MRAE: 0.213665485, Test RMSE: 0.029897889, Test PSNR: 18.968690872
2022-04-24 17:37:06 - Iter[122000], Epoch[000122], learning rate : 0.000258164, Train Loss: 0.141259611, Test MRAE: 0.203018293, Test RMSE: 0.029807677, Test PSNR: 19.059270859
2022-04-24 17:52:52 - Iter[123000], Epoch[000123], learning rate : 0.000256161, Train Loss: 0.140892401, Test MRAE: 0.205029026, Test RMSE: 0.029058052, Test PSNR: 19.114631653
2022-04-24 18:08:38 - Iter[124000], Epoch[000124], learning rate : 0.000254152, Train Loss: 0.140450522, Test MRAE: 0.198189601, Test RMSE: 0.028176619, Test PSNR: 18.899404526
2022-04-24 18:24:25 - Iter[125000], Epoch[000125], learning rate : 0.000252136, Train Loss: 0.140061647, Test MRAE: 0.227593854, Test RMSE: 0.032393444, Test PSNR: 19.117650986
2022-04-24 18:40:12 - Iter[126000], Epoch[000126], learning rate : 0.000250116, Train Loss: 0.139804602, Test MRAE: 0.214596003, Test RMSE: 0.030325968, Test PSNR: 19.085477829
2022-04-24 18:56:03 - Iter[127000], Epoch[000127], learning rate : 0.000248089, Train Loss: 0.139489800, Test MRAE: 0.219026044, Test RMSE: 0.030452706, Test PSNR: 18.957645416
2022-04-24 19:11:50 - Iter[128000], Epoch[000128], learning rate : 0.000246058, Train Loss: 0.139124364, Test MRAE: 0.220908672, Test RMSE: 0.031795617, Test PSNR: 19.092975616
2022-04-24 19:27:36 - Iter[129000], Epoch[000129], learning rate : 0.000244022, Train Loss: 0.138711065, Test MRAE: 0.223648518, Test RMSE: 0.030946231, Test PSNR: 19.023336411
2022-04-24 19:43:24 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.138381705, Test MRAE: 0.199091196, Test RMSE: 0.028440719, Test PSNR: 18.978559494
2022-04-24 19:59:10 - Iter[131000], Epoch[000131], learning rate : 0.000239935, Train Loss: 0.138126105, Test MRAE: 0.200671941, Test RMSE: 0.028158125, Test PSNR: 18.979644775
2022-04-24 20:14:55 - Iter[132000], Epoch[000132], learning rate : 0.000237885, Train Loss: 0.137827173, Test MRAE: 0.203352720, Test RMSE: 0.028771115, Test PSNR: 18.875143051
2022-04-24 20:30:43 - Iter[133000], Epoch[000133], learning rate : 0.000235830, Train Loss: 0.137512103, Test MRAE: 0.251916736, Test RMSE: 0.035541732, Test PSNR: 19.041845322
2022-04-24 20:46:31 - Iter[134000], Epoch[000134], learning rate : 0.000233772, Train Loss: 0.137238741, Test MRAE: 0.229384869, Test RMSE: 0.032080282, Test PSNR: 18.928392410
2022-04-24 21:02:16 - Iter[135000], Epoch[000135], learning rate : 0.000231711, Train Loss: 0.136950210, Test MRAE: 0.230571747, Test RMSE: 0.033738092, Test PSNR: 19.063541412
2022-04-24 21:18:02 - Iter[136000], Epoch[000136], learning rate : 0.000229646, Train Loss: 0.136655480, Test MRAE: 0.210024893, Test RMSE: 0.028892893, Test PSNR: 18.938934326
2022-04-24 21:33:50 - Iter[137000], Epoch[000137], learning rate : 0.000227577, Train Loss: 0.136351779, Test MRAE: 0.199735105, Test RMSE: 0.027150583, Test PSNR: 18.880283356
2022-04-24 21:49:36 - Iter[138000], Epoch[000138], learning rate : 0.000225506, Train Loss: 0.136000752, Test MRAE: 0.213579893, Test RMSE: 0.030251976, Test PSNR: 19.084747314
2022-04-24 22:05:24 - Iter[139000], Epoch[000139], learning rate : 0.000223432, Train Loss: 0.135698289, Test MRAE: 0.202947915, Test RMSE: 0.027996786, Test PSNR: 19.060047150
2022-04-24 22:21:11 - Iter[140000], Epoch[000140], learning rate : 0.000221356, Train Loss: 0.135410920, Test MRAE: 0.204962358, Test RMSE: 0.028149297, Test PSNR: 19.065879822
2022-04-24 22:36:54 - Iter[141000], Epoch[000141], learning rate : 0.000219277, Train Loss: 0.135157645, Test MRAE: 0.203717798, Test RMSE: 0.029486291, Test PSNR: 19.107460022
2022-04-24 22:52:37 - Iter[142000], Epoch[000142], learning rate : 0.000217196, Train Loss: 0.134867549, Test MRAE: 0.224558994, Test RMSE: 0.031202393, Test PSNR: 19.067234039
2022-04-24 23:08:19 - Iter[143000], Epoch[000143], learning rate : 0.000215113, Train Loss: 0.134574100, Test MRAE: 0.211229146, Test RMSE: 0.031165324, Test PSNR: 19.120822906
2022-04-24 23:24:02 - Iter[144000], Epoch[000144], learning rate : 0.000213029, Train Loss: 0.134270564, Test MRAE: 0.215366766, Test RMSE: 0.030734645, Test PSNR: 19.096778870
2022-04-24 23:39:44 - Iter[145000], Epoch[000145], learning rate : 0.000210943, Train Loss: 0.134016097, Test MRAE: 0.198303372, Test RMSE: 0.027785815, Test PSNR: 19.032321930
2022-04-24 23:55:26 - Iter[146000], Epoch[000146], learning rate : 0.000208856, Train Loss: 0.133732110, Test MRAE: 0.196022749, Test RMSE: 0.029352559, Test PSNR: 19.135593414
2022-04-25 00:11:09 - Iter[147000], Epoch[000147], learning rate : 0.000206769, Train Loss: 0.133431599, Test MRAE: 0.209573299, Test RMSE: 0.029887328, Test PSNR: 19.095409393
2022-04-25 00:26:52 - Iter[148000], Epoch[000148], learning rate : 0.000204680, Train Loss: 0.133194283, Test MRAE: 0.208055094, Test RMSE: 0.028258048, Test PSNR: 18.880475998
2022-04-25 00:42:34 - Iter[149000], Epoch[000149], learning rate : 0.000202591, Train Loss: 0.132876396, Test MRAE: 0.223921135, Test RMSE: 0.030200578, Test PSNR: 18.990900040
2022-04-25 00:58:17 - Iter[150000], Epoch[000150], learning rate : 0.000200502, Train Loss: 0.132608861, Test MRAE: 0.214254171, Test RMSE: 0.028446879, Test PSNR: 18.850370407
2022-04-25 01:14:00 - Iter[151000], Epoch[000151], learning rate : 0.000198413, Train Loss: 0.132331938, Test MRAE: 0.213032275, Test RMSE: 0.028355932, Test PSNR: 18.995388031
2022-04-25 01:29:42 - Iter[152000], Epoch[000152], learning rate : 0.000196324, Train Loss: 0.132052571, Test MRAE: 0.209101513, Test RMSE: 0.029273044, Test PSNR: 19.021900177
2022-04-25 01:45:25 - Iter[153000], Epoch[000153], learning rate : 0.000194236, Train Loss: 0.131791100, Test MRAE: 0.210104674, Test RMSE: 0.029532449, Test PSNR: 19.057674408
2022-04-25 02:01:07 - Iter[154000], Epoch[000154], learning rate : 0.000192148, Train Loss: 0.131518036, Test MRAE: 0.212604702, Test RMSE: 0.028673176, Test PSNR: 18.848283768
2022-04-25 02:16:51 - Iter[155000], Epoch[000155], learning rate : 0.000190061, Train Loss: 0.131209671, Test MRAE: 0.210108310, Test RMSE: 0.030193273, Test PSNR: 18.993249893
2022-04-25 02:32:33 - Iter[156000], Epoch[000156], learning rate : 0.000187975, Train Loss: 0.130926698, Test MRAE: 0.214930877, Test RMSE: 0.029281715, Test PSNR: 18.853296280
2022-04-25 02:48:17 - Iter[157000], Epoch[000157], learning rate : 0.000185891, Train Loss: 0.130694464, Test MRAE: 0.211215034, Test RMSE: 0.029316388, Test PSNR: 18.940450668
2022-04-25 03:03:59 - Iter[158000], Epoch[000158], learning rate : 0.000183808, Train Loss: 0.130415007, Test MRAE: 0.202084705, Test RMSE: 0.028342213, Test PSNR: 19.029422760
2022-04-25 03:19:42 - Iter[159000], Epoch[000159], learning rate : 0.000181727, Train Loss: 0.130165070, Test MRAE: 0.200078711, Test RMSE: 0.028258955, Test PSNR: 19.013126373
2022-04-25 03:35:24 - Iter[160000], Epoch[000160], learning rate : 0.000179649, Train Loss: 0.129895121, Test MRAE: 0.201857880, Test RMSE: 0.027987808, Test PSNR: 19.015281677
2022-04-25 03:51:09 - Iter[161000], Epoch[000161], learning rate : 0.000177572, Train Loss: 0.129623845, Test MRAE: 0.219355986, Test RMSE: 0.030731371, Test PSNR: 19.083154678
2022-04-25 04:06:52 - Iter[162000], Epoch[000162], learning rate : 0.000175498, Train Loss: 0.129356831, Test MRAE: 0.206479296, Test RMSE: 0.028123399, Test PSNR: 19.018695831
2022-04-25 04:22:36 - Iter[163000], Epoch[000163], learning rate : 0.000173427, Train Loss: 0.129107624, Test MRAE: 0.198778838, Test RMSE: 0.028552517, Test PSNR: 19.013214111
2022-04-25 04:38:20 - Iter[164000], Epoch[000164], learning rate : 0.000171359, Train Loss: 0.128837064, Test MRAE: 0.195670083, Test RMSE: 0.027478158, Test PSNR: 18.992214203
2022-04-25 04:54:03 - Iter[165000], Epoch[000165], learning rate : 0.000169293, Train Loss: 0.128567725, Test MRAE: 0.211258903, Test RMSE: 0.029616732, Test PSNR: 19.097358704
2022-04-25 05:09:46 - Iter[166000], Epoch[000166], learning rate : 0.000167232, Train Loss: 0.128432900, Test MRAE: 0.191220835, Test RMSE: 0.027260069, Test PSNR: 18.941967010
2022-04-25 05:25:28 - Iter[167000], Epoch[000167], learning rate : 0.000165174, Train Loss: 0.128152385, Test MRAE: 0.202767581, Test RMSE: 0.028542725, Test PSNR: 18.998825073
2022-04-25 05:41:12 - Iter[168000], Epoch[000168], learning rate : 0.000163119, Train Loss: 0.127891243, Test MRAE: 0.204690695, Test RMSE: 0.028364403, Test PSNR: 18.970281601
2022-04-25 05:56:54 - Iter[169000], Epoch[000169], learning rate : 0.000161069, Train Loss: 0.127617165, Test MRAE: 0.203078985, Test RMSE: 0.028361408, Test PSNR: 19.034769058
2022-04-25 06:12:38 - Iter[170000], Epoch[000170], learning rate : 0.000159024, Train Loss: 0.127377793, Test MRAE: 0.206805512, Test RMSE: 0.030018816, Test PSNR: 19.061866760
2022-04-25 06:28:23 - Iter[171000], Epoch[000171], learning rate : 0.000156982, Train Loss: 0.127140224, Test MRAE: 0.208625391, Test RMSE: 0.028507199, Test PSNR: 18.989234924
2022-04-25 06:44:06 - Iter[172000], Epoch[000172], learning rate : 0.000154946, Train Loss: 0.126904130, Test MRAE: 0.210705116, Test RMSE: 0.029785754, Test PSNR: 18.994005203
2022-04-25 06:59:49 - Iter[173000], Epoch[000173], learning rate : 0.000152915, Train Loss: 0.126656905, Test MRAE: 0.210085228, Test RMSE: 0.029623386, Test PSNR: 19.080978394
2022-04-25 07:15:31 - Iter[174000], Epoch[000174], learning rate : 0.000150888, Train Loss: 0.126484647, Test MRAE: 0.215656236, Test RMSE: 0.030040557, Test PSNR: 19.056533813
2022-04-25 07:31:13 - Iter[175000], Epoch[000175], learning rate : 0.000148868, Train Loss: 0.126255050, Test MRAE: 0.216682300, Test RMSE: 0.029841734, Test PSNR: 18.989347458
2022-04-25 07:46:56 - Iter[176000], Epoch[000176], learning rate : 0.000146853, Train Loss: 0.126007512, Test MRAE: 0.229140550, Test RMSE: 0.031297620, Test PSNR: 18.960144043
2022-04-25 08:02:38 - Iter[177000], Epoch[000177], learning rate : 0.000144843, Train Loss: 0.125766575, Test MRAE: 0.218668729, Test RMSE: 0.031003775, Test PSNR: 19.038333893
2022-04-25 08:18:22 - Iter[178000], Epoch[000178], learning rate : 0.000142840, Train Loss: 0.125532001, Test MRAE: 0.199688122, Test RMSE: 0.028291211, Test PSNR: 18.974950790
2022-04-25 08:34:09 - Iter[179000], Epoch[000179], learning rate : 0.000140843, Train Loss: 0.125298381, Test MRAE: 0.207475007, Test RMSE: 0.029013749, Test PSNR: 18.985069275
2022-04-25 08:49:51 - Iter[180000], Epoch[000180], learning rate : 0.000138853, Train Loss: 0.125062704, Test MRAE: 0.206389904, Test RMSE: 0.029368754, Test PSNR: 19.005840302
2022-04-25 09:05:36 - Iter[181000], Epoch[000181], learning rate : 0.000136870, Train Loss: 0.124840498, Test MRAE: 0.210619673, Test RMSE: 0.029867401, Test PSNR: 18.961513519
2022-04-25 09:21:18 - Iter[182000], Epoch[000182], learning rate : 0.000134893, Train Loss: 0.124605544, Test MRAE: 0.220269203, Test RMSE: 0.032067895, Test PSNR: 19.168319702
2022-04-25 09:37:01 - Iter[183000], Epoch[000183], learning rate : 0.000132924, Train Loss: 0.124368042, Test MRAE: 0.191453904, Test RMSE: 0.027029432, Test PSNR: 19.038944244
2022-04-25 09:52:47 - Iter[184000], Epoch[000184], learning rate : 0.000130962, Train Loss: 0.124137081, Test MRAE: 0.206539020, Test RMSE: 0.029571155, Test PSNR: 19.048000336
2022-04-25 10:08:30 - Iter[185000], Epoch[000185], learning rate : 0.000129008, Train Loss: 0.123918340, Test MRAE: 0.194905445, Test RMSE: 0.027700884, Test PSNR: 19.005758286
2022-04-25 10:24:14 - Iter[186000], Epoch[000186], learning rate : 0.000127061, Train Loss: 0.123697750, Test MRAE: 0.205566064, Test RMSE: 0.028691338, Test PSNR: 18.983287811
2022-04-25 10:39:56 - Iter[187000], Epoch[000187], learning rate : 0.000125123, Train Loss: 0.123466983, Test MRAE: 0.201362506, Test RMSE: 0.028842332, Test PSNR: 19.055122375
2022-04-25 10:55:39 - Iter[188000], Epoch[000188], learning rate : 0.000123193, Train Loss: 0.123243488, Test MRAE: 0.206426546, Test RMSE: 0.029135758, Test PSNR: 19.006008148
2022-04-25 11:11:23 - Iter[189000], Epoch[000189], learning rate : 0.000121271, Train Loss: 0.123076245, Test MRAE: 0.207691565, Test RMSE: 0.029229913, Test PSNR: 19.043151855
2022-04-25 11:27:06 - Iter[190000], Epoch[000190], learning rate : 0.000119358, Train Loss: 0.122856230, Test MRAE: 0.206229493, Test RMSE: 0.028870497, Test PSNR: 18.966106415
2022-04-25 11:42:49 - Iter[191000], Epoch[000191], learning rate : 0.000117454, Train Loss: 0.122638315, Test MRAE: 0.205771387, Test RMSE: 0.028803570, Test PSNR: 18.946226120
2022-04-25 11:58:32 - Iter[192000], Epoch[000192], learning rate : 0.000115559, Train Loss: 0.122420080, Test MRAE: 0.212724239, Test RMSE: 0.030358646, Test PSNR: 19.106163025
2022-04-25 12:14:15 - Iter[193000], Epoch[000193], learning rate : 0.000113673, Train Loss: 0.122199051, Test MRAE: 0.214906707, Test RMSE: 0.030220853, Test PSNR: 19.064342499
2022-04-25 12:30:00 - Iter[194000], Epoch[000194], learning rate : 0.000111797, Train Loss: 0.121984355, Test MRAE: 0.198100254, Test RMSE: 0.028148143, Test PSNR: 18.971063614
2022-04-25 12:45:43 - Iter[195000], Epoch[000195], learning rate : 0.000109931, Train Loss: 0.121781416, Test MRAE: 0.198713332, Test RMSE: 0.028723657, Test PSNR: 19.015140533
2022-04-25 13:01:27 - Iter[196000], Epoch[000196], learning rate : 0.000108074, Train Loss: 0.121572427, Test MRAE: 0.197447866, Test RMSE: 0.028784798, Test PSNR: 18.987123489
2022-04-25 13:17:11 - Iter[197000], Epoch[000197], learning rate : 0.000106228, Train Loss: 0.121370003, Test MRAE: 0.205944434, Test RMSE: 0.029543681, Test PSNR: 19.049442291
2022-04-25 13:32:58 - Iter[198000], Epoch[000198], learning rate : 0.000104392, Train Loss: 0.101264954, Test MRAE: 0.202921927, Test RMSE: 0.029191626, Test PSNR: 19.073200226
2022-04-25 13:48:45 - Iter[199000], Epoch[000199], learning rate : 0.000102567, Train Loss: 0.099872775, Test MRAE: 0.213733122, Test RMSE: 0.030334827, Test PSNR: 19.099483490
2022-04-25 14:04:30 - Iter[200000], Epoch[000200], learning rate : 0.000100752, Train Loss: 0.099436894, Test MRAE: 0.207790688, Test RMSE: 0.029580811, Test PSNR: 19.065809250
2022-04-25 14:20:13 - Iter[201000], Epoch[000201], learning rate : 0.000098948, Train Loss: 0.099523008, Test MRAE: 0.206653327, Test RMSE: 0.029507691, Test PSNR: 19.007652283
2022-04-25 14:35:57 - Iter[202000], Epoch[000202], learning rate : 0.000097155, Train Loss: 0.099680349, Test MRAE: 0.209775746, Test RMSE: 0.029743711, Test PSNR: 19.063774109
2022-04-25 14:51:41 - Iter[203000], Epoch[000203], learning rate : 0.000095374, Train Loss: 0.099537373, Test MRAE: 0.205857038, Test RMSE: 0.029820710, Test PSNR: 19.054101944
2022-04-25 15:07:24 - Iter[204000], Epoch[000204], learning rate : 0.000093604, Train Loss: 0.099403314, Test MRAE: 0.201450929, Test RMSE: 0.028907372, Test PSNR: 18.933977127
2022-04-25 15:23:07 - Iter[205000], Epoch[000205], learning rate : 0.000091846, Train Loss: 0.099265657, Test MRAE: 0.202834055, Test RMSE: 0.029253015, Test PSNR: 18.995012283
2022-04-25 15:38:50 - Iter[206000], Epoch[000206], learning rate : 0.000090100, Train Loss: 0.099154606, Test MRAE: 0.200972140, Test RMSE: 0.028723257, Test PSNR: 18.992853165
2022-04-25 15:54:34 - Iter[207000], Epoch[000207], learning rate : 0.000088366, Train Loss: 0.099067487, Test MRAE: 0.206179917, Test RMSE: 0.029294010, Test PSNR: 18.984216690
2022-04-25 16:10:17 - Iter[208000], Epoch[000208], learning rate : 0.000086644, Train Loss: 0.098957688, Test MRAE: 0.201780394, Test RMSE: 0.028410414, Test PSNR: 19.005832672
2022-04-25 16:26:03 - Iter[209000], Epoch[000209], learning rate : 0.000084935, Train Loss: 0.098789133, Test MRAE: 0.209743783, Test RMSE: 0.029414069, Test PSNR: 18.970657349
2022-04-25 16:41:47 - Iter[210000], Epoch[000210], learning rate : 0.000083239, Train Loss: 0.098655112, Test MRAE: 0.200564787, Test RMSE: 0.028275695, Test PSNR: 19.033458710
2022-04-25 16:57:32 - Iter[211000], Epoch[000211], learning rate : 0.000081555, Train Loss: 0.098488487, Test MRAE: 0.199889153, Test RMSE: 0.028281061, Test PSNR: 18.950765610
2022-04-25 17:13:15 - Iter[212000], Epoch[000212], learning rate : 0.000079884, Train Loss: 0.098348401, Test MRAE: 0.200752750, Test RMSE: 0.027866315, Test PSNR: 18.983562469
2022-04-25 17:28:57 - Iter[213000], Epoch[000213], learning rate : 0.000078227, Train Loss: 0.098229684, Test MRAE: 0.201839328, Test RMSE: 0.028493920, Test PSNR: 18.957841873
2022-04-25 17:44:40 - Iter[214000], Epoch[000214], learning rate : 0.000076583, Train Loss: 0.098102383, Test MRAE: 0.204463542, Test RMSE: 0.028648939, Test PSNR: 18.996351242
2022-04-25 18:00:22 - Iter[215000], Epoch[000215], learning rate : 0.000074952, Train Loss: 0.097989276, Test MRAE: 0.201856405, Test RMSE: 0.028056156, Test PSNR: 18.929220200
2022-04-25 18:16:08 - Iter[216000], Epoch[000216], learning rate : 0.000073336, Train Loss: 0.097863868, Test MRAE: 0.210833490, Test RMSE: 0.030079007, Test PSNR: 19.039060593
2022-04-25 18:31:50 - Iter[217000], Epoch[000217], learning rate : 0.000071733, Train Loss: 0.097764373, Test MRAE: 0.199148744, Test RMSE: 0.027379682, Test PSNR: 18.915548325
2022-04-25 18:47:33 - Iter[218000], Epoch[000218], learning rate : 0.000070144, Train Loss: 0.097662948, Test MRAE: 0.211309999, Test RMSE: 0.029880499, Test PSNR: 19.041957855
2022-04-25 19:03:16 - Iter[219000], Epoch[000219], learning rate : 0.000068570, Train Loss: 0.097545773, Test MRAE: 0.202322707, Test RMSE: 0.029350601, Test PSNR: 19.032449722
2022-04-25 19:19:05 - Iter[220000], Epoch[000220], learning rate : 0.000067010, Train Loss: 0.097439609, Test MRAE: 0.201889187, Test RMSE: 0.028135814, Test PSNR: 19.002304077
2022-04-25 19:34:58 - Iter[221000], Epoch[000221], learning rate : 0.000065465, Train Loss: 0.097333550, Test MRAE: 0.205386743, Test RMSE: 0.029404126, Test PSNR: 19.006492615
2022-04-25 19:50:42 - Iter[222000], Epoch[000222], learning rate : 0.000063934, Train Loss: 0.097243309, Test MRAE: 0.200861678, Test RMSE: 0.028495271, Test PSNR: 19.029602051
2022-04-25 20:06:24 - Iter[223000], Epoch[000223], learning rate : 0.000062419, Train Loss: 0.097154871, Test MRAE: 0.209499523, Test RMSE: 0.029613551, Test PSNR: 19.025751114
2022-04-25 20:22:07 - Iter[224000], Epoch[000224], learning rate : 0.000060919, Train Loss: 0.097066604, Test MRAE: 0.216345757, Test RMSE: 0.030452432, Test PSNR: 19.079719543
2022-04-25 20:37:50 - Iter[225000], Epoch[000225], learning rate : 0.000059434, Train Loss: 0.096955277, Test MRAE: 0.207744464, Test RMSE: 0.029243071, Test PSNR: 19.021963120
2022-04-25 20:53:34 - Iter[226000], Epoch[000226], learning rate : 0.000057964, Train Loss: 0.096838258, Test MRAE: 0.199603081, Test RMSE: 0.028276462, Test PSNR: 19.053121567
2022-04-25 21:09:17 - Iter[227000], Epoch[000227], learning rate : 0.000056510, Train Loss: 0.096710235, Test MRAE: 0.197099164, Test RMSE: 0.027980030, Test PSNR: 18.989082336
2022-04-25 21:24:59 - Iter[228000], Epoch[000228], learning rate : 0.000055072, Train Loss: 0.096593060, Test MRAE: 0.192215458, Test RMSE: 0.027695557, Test PSNR: 19.051717758
2022-04-25 21:40:41 - Iter[229000], Epoch[000229], learning rate : 0.000053650, Train Loss: 0.096480407, Test MRAE: 0.194151208, Test RMSE: 0.028371762, Test PSNR: 19.065599442
2022-04-25 21:56:29 - Iter[230000], Epoch[000230], learning rate : 0.000052244, Train Loss: 0.096383542, Test MRAE: 0.203011096, Test RMSE: 0.029121868, Test PSNR: 19.086605072
2022-04-25 22:12:12 - Iter[231000], Epoch[000231], learning rate : 0.000050854, Train Loss: 0.096299224, Test MRAE: 0.201670945, Test RMSE: 0.028482547, Test PSNR: 19.051557541
2022-04-25 22:27:55 - Iter[232000], Epoch[000232], learning rate : 0.000049481, Train Loss: 0.096198440, Test MRAE: 0.196830481, Test RMSE: 0.028049719, Test PSNR: 19.039011002
2022-04-25 22:43:37 - Iter[233000], Epoch[000233], learning rate : 0.000048124, Train Loss: 0.096105792, Test MRAE: 0.194558725, Test RMSE: 0.027864750, Test PSNR: 19.071357727
2022-04-25 22:59:20 - Iter[234000], Epoch[000234], learning rate : 0.000046784, Train Loss: 0.096012853, Test MRAE: 0.200894669, Test RMSE: 0.028464397, Test PSNR: 19.031705856
2022-04-25 23:15:02 - Iter[235000], Epoch[000235], learning rate : 0.000045461, Train Loss: 0.095911391, Test MRAE: 0.203084469, Test RMSE: 0.029015776, Test PSNR: 19.042705536
2022-04-25 23:30:45 - Iter[236000], Epoch[000236], learning rate : 0.000044154, Train Loss: 0.095807374, Test MRAE: 0.205492422, Test RMSE: 0.029720480, Test PSNR: 19.064033508
2022-04-25 23:46:27 - Iter[237000], Epoch[000237], learning rate : 0.000042865, Train Loss: 0.095718473, Test MRAE: 0.200766295, Test RMSE: 0.029130232, Test PSNR: 19.070289612
2022-04-26 00:02:16 - Iter[238000], Epoch[000238], learning rate : 0.000041594, Train Loss: 0.095622800, Test MRAE: 0.201171368, Test RMSE: 0.028642515, Test PSNR: 19.076307297
2022-04-26 00:17:58 - Iter[239000], Epoch[000239], learning rate : 0.000040339, Train Loss: 0.095527329, Test MRAE: 0.207034603, Test RMSE: 0.030098038, Test PSNR: 19.122308731
2022-04-26 00:33:41 - Iter[240000], Epoch[000240], learning rate : 0.000039102, Train Loss: 0.095431149, Test MRAE: 0.200998485, Test RMSE: 0.028509894, Test PSNR: 19.048767090
2022-04-26 00:49:25 - Iter[241000], Epoch[000241], learning rate : 0.000037883, Train Loss: 0.095341481, Test MRAE: 0.197351202, Test RMSE: 0.027994553, Test PSNR: 19.053705215
2022-04-26 01:05:07 - Iter[242000], Epoch[000242], learning rate : 0.000036682, Train Loss: 0.095249861, Test MRAE: 0.205203146, Test RMSE: 0.029421324, Test PSNR: 19.068685532
2022-04-26 01:20:51 - Iter[243000], Epoch[000243], learning rate : 0.000035499, Train Loss: 0.095152855, Test MRAE: 0.202429041, Test RMSE: 0.028778778, Test PSNR: 19.062547684
2022-04-26 01:36:39 - Iter[244000], Epoch[000244], learning rate : 0.000034333, Train Loss: 0.095076188, Test MRAE: 0.199525461, Test RMSE: 0.028577324, Test PSNR: 19.065444946
2022-04-26 01:52:28 - Iter[245000], Epoch[000245], learning rate : 0.000033186, Train Loss: 0.094988756, Test MRAE: 0.193906844, Test RMSE: 0.027360469, Test PSNR: 19.035274506
2022-04-26 02:08:13 - Iter[246000], Epoch[000246], learning rate : 0.000032058, Train Loss: 0.094907209, Test MRAE: 0.194509611, Test RMSE: 0.027639085, Test PSNR: 19.038093567
2022-04-26 02:23:58 - Iter[247000], Epoch[000247], learning rate : 0.000030948, Train Loss: 0.094821684, Test MRAE: 0.205400094, Test RMSE: 0.029638980, Test PSNR: 19.069122314
2022-04-26 02:39:41 - Iter[248000], Epoch[000248], learning rate : 0.000029856, Train Loss: 0.094757117, Test MRAE: 0.200400651, Test RMSE: 0.028777106, Test PSNR: 19.065080643
2022-04-26 02:55:26 - Iter[249000], Epoch[000249], learning rate : 0.000028783, Train Loss: 0.094666235, Test MRAE: 0.196040452, Test RMSE: 0.027733754, Test PSNR: 19.004329681
2022-04-26 03:11:09 - Iter[250000], Epoch[000250], learning rate : 0.000027729, Train Loss: 0.094580248, Test MRAE: 0.198402479, Test RMSE: 0.028335137, Test PSNR: 19.012487411
2022-04-26 03:26:51 - Iter[251000], Epoch[000251], learning rate : 0.000026694, Train Loss: 0.094503880, Test MRAE: 0.198768482, Test RMSE: 0.027874328, Test PSNR: 18.994089127
2022-04-26 03:42:33 - Iter[252000], Epoch[000252], learning rate : 0.000025678, Train Loss: 0.094424129, Test MRAE: 0.204637840, Test RMSE: 0.028599529, Test PSNR: 19.048522949
2022-04-26 03:58:17 - Iter[253000], Epoch[000253], learning rate : 0.000024681, Train Loss: 0.094356239, Test MRAE: 0.197843701, Test RMSE: 0.027720425, Test PSNR: 19.019216537
2022-04-26 04:13:59 - Iter[254000], Epoch[000254], learning rate : 0.000023703, Train Loss: 0.094279803, Test MRAE: 0.195381641, Test RMSE: 0.027696660, Test PSNR: 19.030433655
2022-04-26 04:29:44 - Iter[255000], Epoch[000255], learning rate : 0.000022745, Train Loss: 0.094200850, Test MRAE: 0.201875895, Test RMSE: 0.028370189, Test PSNR: 19.016584396
2022-04-26 04:45:26 - Iter[256000], Epoch[000256], learning rate : 0.000021806, Train Loss: 0.094126128, Test MRAE: 0.203219756, Test RMSE: 0.028974859, Test PSNR: 19.087203979
2022-04-26 05:01:09 - Iter[257000], Epoch[000257], learning rate : 0.000020887, Train Loss: 0.094045103, Test MRAE: 0.194824770, Test RMSE: 0.027946815, Test PSNR: 19.061742783
2022-04-26 05:16:51 - Iter[258000], Epoch[000258], learning rate : 0.000019988, Train Loss: 0.093975663, Test MRAE: 0.198491260, Test RMSE: 0.027982576, Test PSNR: 19.010288239
2022-04-26 05:32:35 - Iter[259000], Epoch[000259], learning rate : 0.000019108, Train Loss: 0.093899436, Test MRAE: 0.201042473, Test RMSE: 0.028101049, Test PSNR: 19.056558609
2022-04-26 05:48:19 - Iter[260000], Epoch[000260], learning rate : 0.000018249, Train Loss: 0.093830153, Test MRAE: 0.201697826, Test RMSE: 0.028358519, Test PSNR: 19.034065247
2022-04-26 06:04:01 - Iter[261000], Epoch[000261], learning rate : 0.000017409, Train Loss: 0.093763351, Test MRAE: 0.200538099, Test RMSE: 0.028340435, Test PSNR: 19.020803452
2022-04-26 06:19:44 - Iter[262000], Epoch[000262], learning rate : 0.000016589, Train Loss: 0.093695477, Test MRAE: 0.202214047, Test RMSE: 0.028496150, Test PSNR: 19.040538788
2022-04-26 06:35:27 - Iter[263000], Epoch[000263], learning rate : 0.000015790, Train Loss: 0.093628220, Test MRAE: 0.199471787, Test RMSE: 0.028235294, Test PSNR: 19.043592453
2022-04-26 06:51:13 - Iter[264000], Epoch[000264], learning rate : 0.000015010, Train Loss: 0.093556948, Test MRAE: 0.200963169, Test RMSE: 0.028326141, Test PSNR: 19.030656815
2022-04-26 07:07:01 - Iter[265000], Epoch[000265], learning rate : 0.000014251, Train Loss: 0.093489110, Test MRAE: 0.197174221, Test RMSE: 0.027766764, Test PSNR: 19.029552460
2022-04-26 07:22:46 - Iter[266000], Epoch[000266], learning rate : 0.000013513, Train Loss: 0.093422472, Test MRAE: 0.198560372, Test RMSE: 0.027845098, Test PSNR: 19.010746002
2022-04-26 07:38:30 - Iter[267000], Epoch[000267], learning rate : 0.000012795, Train Loss: 0.093358673, Test MRAE: 0.198623791, Test RMSE: 0.028192090, Test PSNR: 19.046485901
2022-04-26 07:54:13 - Iter[268000], Epoch[000268], learning rate : 0.000012098, Train Loss: 0.093292192, Test MRAE: 0.197530746, Test RMSE: 0.028094612, Test PSNR: 19.029310226
2022-04-26 08:09:59 - Iter[269000], Epoch[000269], learning rate : 0.000011421, Train Loss: 0.093230203, Test MRAE: 0.199864402, Test RMSE: 0.028338477, Test PSNR: 19.030046463
2022-04-26 08:25:42 - Iter[270000], Epoch[000270], learning rate : 0.000010765, Train Loss: 0.093164317, Test MRAE: 0.196141958, Test RMSE: 0.027876040, Test PSNR: 19.026119232
2022-04-26 08:41:25 - Iter[271000], Epoch[000271], learning rate : 0.000010130, Train Loss: 0.093100064, Test MRAE: 0.199385583, Test RMSE: 0.027962262, Test PSNR: 19.018707275
2022-04-26 08:57:08 - Iter[272000], Epoch[000272], learning rate : 0.000009515, Train Loss: 0.093038529, Test MRAE: 0.203236878, Test RMSE: 0.028776500, Test PSNR: 19.067478180
2022-04-26 09:12:51 - Iter[273000], Epoch[000273], learning rate : 0.000008922, Train Loss: 0.092979573, Test MRAE: 0.201909021, Test RMSE: 0.028371701, Test PSNR: 19.025129318
2022-04-26 09:28:33 - Iter[274000], Epoch[000274], learning rate : 0.000008350, Train Loss: 0.092915766, Test MRAE: 0.199700192, Test RMSE: 0.028185047, Test PSNR: 19.029628754
2022-04-26 09:44:18 - Iter[275000], Epoch[000275], learning rate : 0.000007798, Train Loss: 0.092863925, Test MRAE: 0.199149221, Test RMSE: 0.028089032, Test PSNR: 19.003961563
2022-04-26 10:00:00 - Iter[276000], Epoch[000276], learning rate : 0.000007268, Train Loss: 0.092805415, Test MRAE: 0.196530923, Test RMSE: 0.027886262, Test PSNR: 19.012132645
2022-04-26 10:15:43 - Iter[277000], Epoch[000277], learning rate : 0.000006759, Train Loss: 0.092753656, Test MRAE: 0.197557405, Test RMSE: 0.028128177, Test PSNR: 19.038337708
2022-04-26 10:31:26 - Iter[278000], Epoch[000278], learning rate : 0.000006271, Train Loss: 0.092694871, Test MRAE: 0.197338894, Test RMSE: 0.027873993, Test PSNR: 19.016971588
2022-04-26 10:47:11 - Iter[279000], Epoch[000279], learning rate : 0.000005805, Train Loss: 0.092640802, Test MRAE: 0.199877754, Test RMSE: 0.028381795, Test PSNR: 19.056432724
2022-04-26 11:02:55 - Iter[280000], Epoch[000280], learning rate : 0.000005360, Train Loss: 0.092587359, Test MRAE: 0.198997468, Test RMSE: 0.028246677, Test PSNR: 19.035556793
2022-04-26 11:18:37 - Iter[281000], Epoch[000281], learning rate : 0.000004936, Train Loss: 0.092534050, Test MRAE: 0.198044509, Test RMSE: 0.027995434, Test PSNR: 19.035224915
2022-04-26 11:34:25 - Iter[282000], Epoch[000282], learning rate : 0.000004534, Train Loss: 0.092481837, Test MRAE: 0.199215770, Test RMSE: 0.028157072, Test PSNR: 19.028303146
2022-04-26 11:50:09 - Iter[283000], Epoch[000283], learning rate : 0.000004153, Train Loss: 0.092428215, Test MRAE: 0.199768141, Test RMSE: 0.028234014, Test PSNR: 19.032674789
2022-04-26 12:05:52 - Iter[284000], Epoch[000284], learning rate : 0.000003794, Train Loss: 0.092372514, Test MRAE: 0.200132683, Test RMSE: 0.028386112, Test PSNR: 19.050033569
2022-04-26 12:21:34 - Iter[285000], Epoch[000285], learning rate : 0.000003457, Train Loss: 0.092325777, Test MRAE: 0.200290814, Test RMSE: 0.028357379, Test PSNR: 19.042633057
2022-04-26 12:37:16 - Iter[286000], Epoch[000286], learning rate : 0.000003140, Train Loss: 0.092276767, Test MRAE: 0.199622378, Test RMSE: 0.028264590, Test PSNR: 19.027994156
2022-04-26 12:52:59 - Iter[287000], Epoch[000287], learning rate : 0.000002846, Train Loss: 0.092229761, Test MRAE: 0.200462580, Test RMSE: 0.028426396, Test PSNR: 19.040649414
2022-04-26 13:08:45 - Iter[288000], Epoch[000288], learning rate : 0.000002573, Train Loss: 0.092182025, Test MRAE: 0.199467480, Test RMSE: 0.028248770, Test PSNR: 19.024812698
2022-04-26 13:24:28 - Iter[289000], Epoch[000289], learning rate : 0.000002322, Train Loss: 0.092134498, Test MRAE: 0.199127629, Test RMSE: 0.028083034, Test PSNR: 19.012405396
2022-04-26 13:40:10 - Iter[290000], Epoch[000290], learning rate : 0.000002093, Train Loss: 0.092089295, Test MRAE: 0.200336218, Test RMSE: 0.028368756, Test PSNR: 19.028881073
2022-04-26 13:55:54 - Iter[291000], Epoch[000291], learning rate : 0.000001886, Train Loss: 0.092047319, Test MRAE: 0.198826462, Test RMSE: 0.028130181, Test PSNR: 19.023952484
2022-04-26 14:11:39 - Iter[292000], Epoch[000292], learning rate : 0.000001700, Train Loss: 0.092004254, Test MRAE: 0.200323239, Test RMSE: 0.028369704, Test PSNR: 19.032167435
2022-04-26 14:27:22 - Iter[293000], Epoch[000293], learning rate : 0.000001536, Train Loss: 0.091962963, Test MRAE: 0.198782578, Test RMSE: 0.028092362, Test PSNR: 19.022401810
2022-04-26 14:43:07 - Iter[294000], Epoch[000294], learning rate : 0.000001394, Train Loss: 0.091920927, Test MRAE: 0.199555337, Test RMSE: 0.028241893, Test PSNR: 19.032341003
2022-04-26 14:58:52 - Iter[295000], Epoch[000295], learning rate : 0.000001274, Train Loss: 0.091873795, Test MRAE: 0.200663373, Test RMSE: 0.028400686, Test PSNR: 19.039638519
2022-04-26 15:14:36 - Iter[296000], Epoch[000296], learning rate : 0.000001175, Train Loss: 0.091835335, Test MRAE: 0.199872047, Test RMSE: 0.028326735, Test PSNR: 19.036495209
2022-04-26 15:30:21 - Iter[297000], Epoch[000297], learning rate : 0.000001099, Train Loss: 0.087091446, Test MRAE: 0.200016499, Test RMSE: 0.028326962, Test PSNR: 19.033159256
2022-04-26 15:46:09 - Iter[298000], Epoch[000298], learning rate : 0.000001044, Train Loss: 0.087477118, Test MRAE: 0.199742630, Test RMSE: 0.028276479, Test PSNR: 19.029752731
2022-04-26 16:01:52 - Iter[299000], Epoch[000299], learning rate : 0.000001011, Train Loss: 0.087522693, Test MRAE: 0.200146362, Test RMSE: 0.028322442, Test PSNR: 19.031415939
2022-04-26 16:17:34 - Iter[300000], Epoch[000300], learning rate : 0.000001000, Train Loss: 0.087497599, Test MRAE: 0.200221285, Test RMSE: 0.028348781, Test PSNR: 19.032201767
2022-04-26 16:33:17 - Iter[301000], Epoch[000301], learning rate : 0.000001011, Train Loss: 0.087575421, Test MRAE: 0.200616375, Test RMSE: 0.028419724, Test PSNR: 19.031145096
2022-04-26 16:49:01 - Iter[302000], Epoch[000302], learning rate : 0.000001044, Train Loss: 0.087664612, Test MRAE: 0.199603871, Test RMSE: 0.028222578, Test PSNR: 19.027841568
2022-04-26 17:04:44 - Iter[303000], Epoch[000303], learning rate : 0.000001098, Train Loss: 0.087709896, Test MRAE: 0.199289769, Test RMSE: 0.028206011, Test PSNR: 19.033077240
2022-04-26 17:20:27 - Iter[304000], Epoch[000304], learning rate : 0.000001175, Train Loss: 0.087701336, Test MRAE: 0.198439360, Test RMSE: 0.028052971, Test PSNR: 19.023553848
2022-04-26 17:36:09 - Iter[305000], Epoch[000305], learning rate : 0.000001273, Train Loss: 0.087724887, Test MRAE: 0.199188337, Test RMSE: 0.028147060, Test PSNR: 19.024749756
2022-04-26 17:51:52 - Iter[306000], Epoch[000306], learning rate : 0.000001394, Train Loss: 0.087697797, Test MRAE: 0.198744774, Test RMSE: 0.028123770, Test PSNR: 19.021911621
2022-04-26 18:07:40 - Iter[307000], Epoch[000307], learning rate : 0.000001536, Train Loss: 0.087724082, Test MRAE: 0.198784173, Test RMSE: 0.028199598, Test PSNR: 19.027591705
2022-04-26 18:23:26 - Iter[308000], Epoch[000308], learning rate : 0.000001699, Train Loss: 0.087754786, Test MRAE: 0.200074822, Test RMSE: 0.028291941, Test PSNR: 19.026384354
2022-04-26 18:39:09 - Iter[309000], Epoch[000309], learning rate : 0.000001885, Train Loss: 0.087743573, Test MRAE: 0.199570522, Test RMSE: 0.028250307, Test PSNR: 19.019496918
2022-04-26 18:54:53 - Iter[310000], Epoch[000310], learning rate : 0.000002093, Train Loss: 0.087756194, Test MRAE: 0.200466946, Test RMSE: 0.028466217, Test PSNR: 19.043897629
2022-04-26 19:10:36 - Iter[311000], Epoch[000311], learning rate : 0.000002322, Train Loss: 0.087725841, Test MRAE: 0.200274870, Test RMSE: 0.028353559, Test PSNR: 19.034431458
2022-04-26 19:26:18 - Iter[312000], Epoch[000312], learning rate : 0.000002573, Train Loss: 0.087727122, Test MRAE: 0.199417949, Test RMSE: 0.028240953, Test PSNR: 19.032062531
2022-04-26 19:42:00 - Iter[313000], Epoch[000313], learning rate : 0.000002846, Train Loss: 0.087730475, Test MRAE: 0.200596809, Test RMSE: 0.028354665, Test PSNR: 19.033941269
2022-04-26 19:57:43 - Iter[314000], Epoch[000314], learning rate : 0.000003140, Train Loss: 0.087749563, Test MRAE: 0.201247305, Test RMSE: 0.028494956, Test PSNR: 19.038160324
2022-04-26 20:13:26 - Iter[315000], Epoch[000315], learning rate : 0.000003456, Train Loss: 0.087754004, Test MRAE: 0.200625256, Test RMSE: 0.028311061, Test PSNR: 19.022411346
2022-04-26 20:29:09 - Iter[316000], Epoch[000316], learning rate : 0.000003793, Train Loss: 0.087767355, Test MRAE: 0.199434310, Test RMSE: 0.028191015, Test PSNR: 19.015176773
2022-04-26 20:44:59 - Iter[317000], Epoch[000317], learning rate : 0.000004153, Train Loss: 0.087786980, Test MRAE: 0.201855198, Test RMSE: 0.028614521, Test PSNR: 19.041559219
2022-04-26 21:00:41 - Iter[318000], Epoch[000318], learning rate : 0.000004533, Train Loss: 0.087780491, Test MRAE: 0.200222835, Test RMSE: 0.028176807, Test PSNR: 19.015808105
2022-04-26 21:16:24 - Iter[319000], Epoch[000319], learning rate : 0.000004935, Train Loss: 0.087792754, Test MRAE: 0.199134097, Test RMSE: 0.028215753, Test PSNR: 19.028671265
2022-04-26 21:32:06 - Iter[320000], Epoch[000320], learning rate : 0.000005359, Train Loss: 0.087798759, Test MRAE: 0.200270444, Test RMSE: 0.028273745, Test PSNR: 19.037963867
2022-04-26 21:47:48 - Iter[321000], Epoch[000321], learning rate : 0.000005804, Train Loss: 0.087813973, Test MRAE: 0.200781301, Test RMSE: 0.028401980, Test PSNR: 19.028430939
2022-04-26 22:03:33 - Iter[322000], Epoch[000322], learning rate : 0.000006271, Train Loss: 0.087820567, Test MRAE: 0.199449122, Test RMSE: 0.028095623, Test PSNR: 19.015094757
2022-04-26 22:19:15 - Iter[323000], Epoch[000323], learning rate : 0.000006758, Train Loss: 0.087827265, Test MRAE: 0.201382726, Test RMSE: 0.028672023, Test PSNR: 19.032772064
2022-04-26 22:34:57 - Iter[324000], Epoch[000324], learning rate : 0.000007267, Train Loss: 0.087820075, Test MRAE: 0.199561149, Test RMSE: 0.028316684, Test PSNR: 19.027658463
2022-04-26 22:50:42 - Iter[325000], Epoch[000325], learning rate : 0.000007797, Train Loss: 0.087809280, Test MRAE: 0.202198595, Test RMSE: 0.028633879, Test PSNR: 19.034700394
2022-04-26 23:06:24 - Iter[326000], Epoch[000326], learning rate : 0.000008349, Train Loss: 0.087817691, Test MRAE:

Yuanhao Cai · Answer 1 · Sun May 08 2022 21:03:42 GMT+0800 (China Standard Time)

你好，我们这边MST训练和测试一切正常，以下是我们的 training log，我们取的是第100个epoch。

你的log看上去，RMSE和MRAE都没问题，可以把你的预训练模型（你的第103个epoch）发到我的邮箱，我帮你debug。

2022-04-02 11:34:14 - Iter[001000], Epoch[000001], learning rate : 0.000399989, Train Loss: 0.620418608, Test MRAE: 0.560583055, Test RMSE: 0.093895465, Test PSNR: 23.395589828
2022-04-02 12:06:36 - Iter[002000], Epoch[000002], learning rate : 0.000399956, Train Loss: 0.562427759, Test MRAE: 0.492005080, Test RMSE: 0.083448417, Test PSNR: 24.118293762
2022-04-02 12:39:00 - Iter[003000], Epoch[000003], learning rate : 0.000399902, Train Loss: 0.534111321, Test MRAE: 0.449874640, Test RMSE: 0.083608247, Test PSNR: 24.674308777
2022-04-02 13:11:25 - Iter[004000], Epoch[000004], learning rate : 0.000399825, Train Loss: 0.516225159, Test MRAE: 0.347598106, Test RMSE: 0.053511366, Test PSNR: 27.611690521
2022-04-02 13:43:51 - Iter[005000], Epoch[000005], learning rate : 0.000399727, Train Loss: 0.502314687, Test MRAE: 0.396228433, Test RMSE: 0.067197815, Test PSNR: 25.962589264
2022-04-02 14:16:15 - Iter[006000], Epoch[000006], learning rate : 0.000399606, Train Loss: 0.491025537, Test MRAE: 0.339778692, Test RMSE: 0.054121889, Test PSNR: 27.670053482
2022-04-02 14:48:36 - Iter[007000], Epoch[000007], learning rate : 0.000399464, Train Loss: 0.482131004, Test MRAE: 0.323413223, Test RMSE: 0.049012914, Test PSNR: 28.042488098
2022-04-02 15:21:00 - Iter[008000], Epoch[000008], learning rate : 0.000399301, Train Loss: 0.475396514, Test MRAE: 0.309809148, Test RMSE: 0.047974061, Test PSNR: 28.608776093
2022-04-02 15:53:26 - Iter[009000], Epoch[000009], learning rate : 0.000399115, Train Loss: 0.468683064, Test MRAE: 0.343073189, Test RMSE: 0.057604246, Test PSNR: 27.595987320
2022-04-02 16:25:49 - Iter[010000], Epoch[000010], learning rate : 0.000398907, Train Loss: 0.462774545, Test MRAE: 0.409300178, Test RMSE: 0.069563881, Test PSNR: 25.913831711
2022-04-02 16:58:12 - Iter[011000], Epoch[000011], learning rate : 0.000398678, Train Loss: 0.454625964, Test MRAE: 0.347226471, Test RMSE: 0.054569583, Test PSNR: 27.493247986
2022-04-02 17:30:39 - Iter[012000], Epoch[000012], learning rate : 0.000398427, Train Loss: 0.445717156, Test MRAE: 0.297915250, Test RMSE: 0.043623909, Test PSNR: 29.096220016
2022-04-02 18:03:01 - Iter[013000], Epoch[000013], learning rate : 0.000398154, Train Loss: 0.436778575, Test MRAE: 0.350511670, Test RMSE: 0.054038201, Test PSNR: 27.508705139
2022-04-02 18:35:25 - Iter[014000], Epoch[000014], learning rate : 0.000397860, Train Loss: 0.428399771, Test MRAE: 0.271549225, Test RMSE: 0.041802082, Test PSNR: 29.828725815
2022-04-02 19:07:51 - Iter[015000], Epoch[000015], learning rate : 0.000397544, Train Loss: 0.420415014, Test MRAE: 0.229911402, Test RMSE: 0.032794945, Test PSNR: 31.410335541
2022-04-02 19:40:12 - Iter[016000], Epoch[000016], learning rate : 0.000397207, Train Loss: 0.413060188, Test MRAE: 0.233368352, Test RMSE: 0.034829330, Test PSNR: 31.159816742
2022-04-02 20:12:33 - Iter[017000], Epoch[000017], learning rate : 0.000396847, Train Loss: 0.406036615, Test MRAE: 0.253887236, Test RMSE: 0.036092710, Test PSNR: 30.788242340
2022-04-02 20:44:57 - Iter[018000], Epoch[000018], learning rate : 0.000396467, Train Loss: 0.399467081, Test MRAE: 0.275199234, Test RMSE: 0.038107593, Test PSNR: 30.738962173
2022-04-02 21:17:21 - Iter[019000], Epoch[000019], learning rate : 0.000396065, Train Loss: 0.393266708, Test MRAE: 0.274056435, Test RMSE: 0.041836198, Test PSNR: 29.754499435
2022-04-02 21:49:46 - Iter[020000], Epoch[000020], learning rate : 0.000395641, Train Loss: 0.387227565, Test MRAE: 0.254053324, Test RMSE: 0.036078855, Test PSNR: 30.798976898
2022-04-02 22:22:13 - Iter[021000], Epoch[000021], learning rate : 0.000395196, Train Loss: 0.381896675, Test MRAE: 0.317263842, Test RMSE: 0.050258230, Test PSNR: 28.193664551
2022-04-02 22:54:39 - Iter[022000], Epoch[000022], learning rate : 0.000394729, Train Loss: 0.376830012, Test MRAE: 0.261554778, Test RMSE: 0.037159152, Test PSNR: 30.413949966
2022-04-02 23:27:03 - Iter[023000], Epoch[000023], learning rate : 0.000394242, Train Loss: 0.371974677, Test MRAE: 0.231530771, Test RMSE: 0.033456005, Test PSNR: 31.311767578
2022-04-02 23:59:26 - Iter[024000], Epoch[000024], learning rate : 0.000393733, Train Loss: 0.367381543, Test MRAE: 0.230648890, Test RMSE: 0.033315822, Test PSNR: 31.386203766
2022-04-03 00:31:49 - Iter[025000], Epoch[000025], learning rate : 0.000393203, Train Loss: 0.363164872, Test MRAE: 0.252955765, Test RMSE: 0.037958100, Test PSNR: 30.384708405
2022-04-03 01:04:13 - Iter[026000], Epoch[000026], learning rate : 0.000392651, Train Loss: 0.358833164, Test MRAE: 0.272806436, Test RMSE: 0.036007080, Test PSNR: 30.569780350
2022-04-03 01:36:41 - Iter[027000], Epoch[000027], learning rate : 0.000392079, Train Loss: 0.354826480, Test MRAE: 0.243282825, Test RMSE: 0.034428399, Test PSNR: 31.098934174
2022-04-03 02:09:06 - Iter[028000], Epoch[000028], learning rate : 0.000391486, Train Loss: 0.351013869, Test MRAE: 0.201230079, Test RMSE: 0.028252291, Test PSNR: 32.641658783
2022-04-03 02:41:28 - Iter[029000], Epoch[000029], learning rate : 0.000390872, Train Loss: 0.347357094, Test MRAE: 0.242538407, Test RMSE: 0.034608122, Test PSNR: 30.934244156
2022-04-03 03:13:52 - Iter[030000], Epoch[000030], learning rate : 0.000390236, Train Loss: 0.343761057, Test MRAE: 0.192593381, Test RMSE: 0.028935008, Test PSNR: 32.857242584
2022-04-03 03:46:19 - Iter[031000], Epoch[000031], learning rate : 0.000389580, Train Loss: 0.340367079, Test MRAE: 0.267615825, Test RMSE: 0.038927425, Test PSNR: 29.974081039
2022-04-03 04:18:42 - Iter[032000], Epoch[000032], learning rate : 0.000388904, Train Loss: 0.337215900, Test MRAE: 0.214371651, Test RMSE: 0.033283573, Test PSNR: 31.738494873
2022-04-03 04:51:08 - Iter[033000], Epoch[000033], learning rate : 0.000388206, Train Loss: 0.334108531, Test MRAE: 0.262079358, Test RMSE: 0.035866227, Test PSNR: 30.661380768
2022-04-03 05:23:36 - Iter[034000], Epoch[000034], learning rate : 0.000387488, Train Loss: 0.331190139, Test MRAE: 0.270125687, Test RMSE: 0.039441813, Test PSNR: 30.184894562
2022-04-03 05:56:01 - Iter[035000], Epoch[000035], learning rate : 0.000386750, Train Loss: 0.328170478, Test MRAE: 0.249807626, Test RMSE: 0.035371449, Test PSNR: 31.095199585
2022-04-03 06:28:26 - Iter[036000], Epoch[000036], learning rate : 0.000385991, Train Loss: 0.325303972, Test MRAE: 0.233958453, Test RMSE: 0.032760877, Test PSNR: 31.700361252
2022-04-03 07:00:52 - Iter[037000], Epoch[000037], learning rate : 0.000385212, Train Loss: 0.322538584, Test MRAE: 0.218890682, Test RMSE: 0.031196220, Test PSNR: 31.967128754
2022-04-03 07:33:20 - Iter[038000], Epoch[000038], learning rate : 0.000384413, Train Loss: 0.319857717, Test MRAE: 0.221374378, Test RMSE: 0.033305220, Test PSNR: 31.648876190
2022-04-03 08:05:49 - Iter[039000], Epoch[000039], learning rate : 0.000383593, Train Loss: 0.317282826, Test MRAE: 0.221022919, Test RMSE: 0.030678451, Test PSNR: 32.159168243
2022-04-03 08:38:17 - Iter[040000], Epoch[000040], learning rate : 0.000382753, Train Loss: 0.314783841, Test MRAE: 0.223520592, Test RMSE: 0.034221381, Test PSNR: 31.371171951
2022-04-03 09:10:41 - Iter[041000], Epoch[000041], learning rate : 0.000381893, Train Loss: 0.312307864, Test MRAE: 0.185600996, Test RMSE: 0.027297905, Test PSNR: 33.322154999
2022-04-03 09:43:05 - Iter[042000], Epoch[000042], learning rate : 0.000381014, Train Loss: 0.309948951, Test MRAE: 0.187491834, Test RMSE: 0.027875733, Test PSNR: 33.105575562
2022-04-03 10:15:32 - Iter[043000], Epoch[000043], learning rate : 0.000380115, Train Loss: 0.307733774, Test MRAE: 0.197570801, Test RMSE: 0.028535316, Test PSNR: 32.762901306
2022-04-03 10:47:59 - Iter[044000], Epoch[000044], learning rate : 0.000379195, Train Loss: 0.305511564, Test MRAE: 0.226875067, Test RMSE: 0.034113593, Test PSNR: 31.265346527
2022-04-03 11:20:28 - Iter[045000], Epoch[000045], learning rate : 0.000378257, Train Loss: 0.303314179, Test MRAE: 0.188325718, Test RMSE: 0.028871169, Test PSNR: 32.739334106
2022-04-03 11:52:52 - Iter[046000], Epoch[000046], learning rate : 0.000377299, Train Loss: 0.301184267, Test MRAE: 0.209558234, Test RMSE: 0.030309312, Test PSNR: 32.249511719
2022-04-03 12:25:15 - Iter[047000], Epoch[000047], learning rate : 0.000376321, Train Loss: 0.299149603, Test MRAE: 0.181863576, Test RMSE: 0.027356362, Test PSNR: 33.194358826
2022-04-03 12:57:40 - Iter[048000], Epoch[000048], learning rate : 0.000375324, Train Loss: 0.297067881, Test MRAE: 0.190021947, Test RMSE: 0.031397291, Test PSNR: 32.164791107
2022-04-03 13:30:02 - Iter[049000], Epoch[000049], learning rate : 0.000374308, Train Loss: 0.295079380, Test MRAE: 0.183715492, Test RMSE: 0.028787576, Test PSNR: 32.805923462
2022-04-03 14:02:28 - Iter[050000], Epoch[000050], learning rate : 0.000373273, Train Loss: 0.293135762, Test MRAE: 0.220063165, Test RMSE: 0.031234333, Test PSNR: 32.020130157
2022-04-03 14:34:55 - Iter[051000], Epoch[000051], learning rate : 0.000372219, Train Loss: 0.291240394, Test MRAE: 0.196562096, Test RMSE: 0.028778845, Test PSNR: 32.885463715
2022-04-03 15:07:23 - Iter[052000], Epoch[000052], learning rate : 0.000371146, Train Loss: 0.289423347, Test MRAE: 0.219960719, Test RMSE: 0.032957252, Test PSNR: 31.785587311
2022-04-03 15:39:49 - Iter[053000], Epoch[000053], learning rate : 0.000370055, Train Loss: 0.287650466, Test MRAE: 0.204551309, Test RMSE: 0.032123860, Test PSNR: 31.762102127
2022-04-03 16:12:12 - Iter[054000], Epoch[000054], learning rate : 0.000368945, Train Loss: 0.285881579, Test MRAE: 0.233348593, Test RMSE: 0.033383630, Test PSNR: 31.642343521
2022-04-03 16:44:37 - Iter[055000], Epoch[000055], learning rate : 0.000367816, Train Loss: 0.284139544, Test MRAE: 0.190262243, Test RMSE: 0.027233521, Test PSNR: 33.065544128
2022-04-03 17:17:01 - Iter[056000], Epoch[000056], learning rate : 0.000366669, Train Loss: 0.282488197, Test MRAE: 0.222938895, Test RMSE: 0.033019535, Test PSNR: 31.726907730
2022-04-03 17:49:26 - Iter[057000], Epoch[000057], learning rate : 0.000365504, Train Loss: 0.280943066, Test MRAE: 0.191757441, Test RMSE: 0.026533134, Test PSNR: 33.209274292
2022-04-03 18:21:56 - Iter[058000], Epoch[000058], learning rate : 0.000364320, Train Loss: 0.279363304, Test MRAE: 0.191072673, Test RMSE: 0.026656503, Test PSNR: 33.171718597
2022-04-03 18:54:27 - Iter[059000], Epoch[000059], learning rate : 0.000363119, Train Loss: 0.277820468, Test MRAE: 0.221181542, Test RMSE: 0.031675849, Test PSNR: 31.875541687
2022-04-03 19:26:54 - Iter[060000], Epoch[000060], learning rate : 0.000361900, Train Loss: 0.276275545, Test MRAE: 0.206039786, Test RMSE: 0.030313989, Test PSNR: 32.258655548
2022-04-03 19:59:19 - Iter[061000], Epoch[000061], learning rate : 0.000360663, Train Loss: 0.274796337, Test MRAE: 0.204649851, Test RMSE: 0.028015587, Test PSNR: 32.868324280
2022-04-03 20:31:46 - Iter[062000], Epoch[000062], learning rate : 0.000359409, Train Loss: 0.273266792, Test MRAE: 0.224741861, Test RMSE: 0.031555891, Test PSNR: 31.983673096
2022-04-03 20:55:16 - Iter[063000], Epoch[000063], learning rate : 0.000358137, Train Loss: 0.271817923, Test MRAE: 0.189551935, Test RMSE: 0.028851932, Test PSNR: 32.851345062
2022-04-03 21:10:21 - Iter[064000], Epoch[000064], learning rate : 0.000356848, Train Loss: 0.270400584, Test MRAE: 0.203208402, Test RMSE: 0.028808346, Test PSNR: 32.608386993
2022-04-03 21:25:27 - Iter[065000], Epoch[000065], learning rate : 0.000355542, Train Loss: 0.269028574, Test MRAE: 0.220868349, Test RMSE: 0.031925108, Test PSNR: 31.762769699
2022-04-03 21:40:31 - Iter[066000], Epoch[000066], learning rate : 0.000354219, Train Loss: 0.267666906, Test MRAE: 0.193385214, Test RMSE: 0.028242324, Test PSNR: 33.030384064
2022-04-03 21:55:36 - Iter[067000], Epoch[000067], learning rate : 0.000352879, Train Loss: 0.266314149, Test MRAE: 0.237542555, Test RMSE: 0.033729393, Test PSNR: 31.420392990
2022-04-03 22:10:39 - Iter[068000], Epoch[000068], learning rate : 0.000351522, Train Loss: 0.265023798, Test MRAE: 0.218250886, Test RMSE: 0.031805661, Test PSNR: 31.864067078
2022-04-03 22:25:43 - Iter[069000], Epoch[000069], learning rate : 0.000350149, Train Loss: 0.263761312, Test MRAE: 0.222390249, Test RMSE: 0.033285633, Test PSNR: 31.789407730
2022-04-03 22:40:47 - Iter[070000], Epoch[000070], learning rate : 0.000348759, Train Loss: 0.262447029, Test MRAE: 0.196066216, Test RMSE: 0.029401546, Test PSNR: 32.656547546
2022-04-03 22:55:50 - Iter[071000], Epoch[000071], learning rate : 0.000347353, Train Loss: 0.261158377, Test MRAE: 0.200198889, Test RMSE: 0.028752174, Test PSNR: 32.740413666
2022-04-03 23:10:53 - Iter[072000], Epoch[000072], learning rate : 0.000345931, Train Loss: 0.259994119, Test MRAE: 0.237112552, Test RMSE: 0.032849517, Test PSNR: 31.563541412
2022-04-03 23:25:57 - Iter[073000], Epoch[000073], learning rate : 0.000344493, Train Loss: 0.258760482, Test MRAE: 0.229209989, Test RMSE: 0.032723866, Test PSNR: 31.701129913
2022-04-03 23:41:01 - Iter[074000], Epoch[000074], learning rate : 0.000343039, Train Loss: 0.257559538, Test MRAE: 0.204919517, Test RMSE: 0.030268239, Test PSNR: 32.507282257
2022-04-03 23:56:06 - Iter[075000], Epoch[000075], learning rate : 0.000341569, Train Loss: 0.256368399, Test MRAE: 0.181171849, Test RMSE: 0.025950298, Test PSNR: 33.887706757
2022-04-04 00:11:12 - Iter[076000], Epoch[000076], learning rate : 0.000340084, Train Loss: 0.255198270, Test MRAE: 0.199154645, Test RMSE: 0.027702348, Test PSNR: 32.698928833
2022-04-04 00:26:17 - Iter[077000], Epoch[000077], learning rate : 0.000338584, Train Loss: 0.254065841, Test MRAE: 0.203430235, Test RMSE: 0.028538819, Test PSNR: 32.758377075
2022-04-04 00:41:23 - Iter[078000], Epoch[000078], learning rate : 0.000337069, Train Loss: 0.252917409, Test MRAE: 0.216813579, Test RMSE: 0.030830607, Test PSNR: 32.058784485
2022-04-04 00:56:29 - Iter[079000], Epoch[000079], learning rate : 0.000335538, Train Loss: 0.251813143, Test MRAE: 0.185084179, Test RMSE: 0.027763113, Test PSNR: 33.090721130
2022-04-04 01:11:33 - Iter[080000], Epoch[000080], learning rate : 0.000333993, Train Loss: 0.250687212, Test MRAE: 0.209134921, Test RMSE: 0.029879550, Test PSNR: 32.484687805
2022-04-04 01:26:38 - Iter[081000], Epoch[000081], learning rate : 0.000332433, Train Loss: 0.249629155, Test MRAE: 0.195731774, Test RMSE: 0.028443594, Test PSNR: 32.708835602
2022-04-04 01:41:43 - Iter[082000], Epoch[000082], learning rate : 0.000330859, Train Loss: 0.248632759, Test MRAE: 0.195523217, Test RMSE: 0.028274089, Test PSNR: 32.682769775
2022-04-04 01:56:47 - Iter[083000], Epoch[000083], learning rate : 0.000329270, Train Loss: 0.247580260, Test MRAE: 0.209766090, Test RMSE: 0.030166527, Test PSNR: 32.471466064
2022-04-04 02:11:51 - Iter[084000], Epoch[000084], learning rate : 0.000327668, Train Loss: 0.246552005, Test MRAE: 0.204757005, Test RMSE: 0.027665943, Test PSNR: 33.038761139
2022-04-04 02:26:55 - Iter[085000], Epoch[000085], learning rate : 0.000326051, Train Loss: 0.245557860, Test MRAE: 0.202718705, Test RMSE: 0.029708728, Test PSNR: 32.356388092
2022-04-04 02:41:59 - Iter[086000], Epoch[000086], learning rate : 0.000324421, Train Loss: 0.244576335, Test MRAE: 0.178362995, Test RMSE: 0.025469040, Test PSNR: 33.612102509
2022-04-04 02:57:03 - Iter[087000], Epoch[000087], learning rate : 0.000322777, Train Loss: 0.243570864, Test MRAE: 0.193244651, Test RMSE: 0.028349675, Test PSNR: 32.873966217
2022-04-04 03:12:08 - Iter[088000], Epoch[000088], learning rate : 0.000321119, Train Loss: 0.242588922, Test MRAE: 0.194302976, Test RMSE: 0.028424148, Test PSNR: 32.846477509
2022-04-04 03:27:14 - Iter[089000], Epoch[000089], learning rate : 0.000319449, Train Loss: 0.241630048, Test MRAE: 0.201137885, Test RMSE: 0.028588405, Test PSNR: 32.868373871
2022-04-04 03:42:20 - Iter[090000], Epoch[000090], learning rate : 0.000317765, Train Loss: 0.240691990, Test MRAE: 0.204772845, Test RMSE: 0.029571073, Test PSNR: 32.514541626
2022-04-04 03:57:25 - Iter[091000], Epoch[000091], learning rate : 0.000316068, Train Loss: 0.239766181, Test MRAE: 0.217822865, Test RMSE: 0.030449659, Test PSNR: 31.977256775
2022-04-04 04:12:30 - Iter[092000], Epoch[000092], learning rate : 0.000314359, Train Loss: 0.238845125, Test MRAE: 0.194552243, Test RMSE: 0.027086474, Test PSNR: 33.231258392
2022-04-04 04:27:35 - Iter[093000], Epoch[000093], learning rate : 0.000312637, Train Loss: 0.237907052, Test MRAE: 0.213251933, Test RMSE: 0.030768594, Test PSNR: 32.156658173
2022-04-04 04:42:39 - Iter[094000], Epoch[000094], learning rate : 0.000310903, Train Loss: 0.237007573, Test MRAE: 0.219156489, Test RMSE: 0.029847248, Test PSNR: 32.261379242
2022-04-04 04:57:43 - Iter[095000], Epoch[000095], learning rate : 0.000309157, Train Loss: 0.236110017, Test MRAE: 0.186253712, Test RMSE: 0.028041288, Test PSNR: 32.811218262
2022-04-04 05:12:47 - Iter[096000], Epoch[000096], learning rate : 0.000307399, Train Loss: 0.235270724, Test MRAE: 0.186458349, Test RMSE: 0.026037846, Test PSNR: 33.781459808
2022-04-04 05:27:50 - Iter[097000], Epoch[000097], learning rate : 0.000305629, Train Loss: 0.234423935, Test MRAE: 0.200644925, Test RMSE: 0.027130893, Test PSNR: 33.330913544
2022-04-04 05:42:54 - Iter[098000], Epoch[000098], learning rate : 0.000303848, Train Loss: 0.233564392, Test MRAE: 0.200904399, Test RMSE: 0.028958160, Test PSNR: 32.430629730
2022-04-04 05:58:00 - Iter[099000], Epoch[000099], learning rate : 0.000302056, Train Loss: 0.145179063, Test MRAE: 0.187149137, Test RMSE: 0.026091099, Test PSNR: 33.442146301
2022-04-04 06:13:05 - Iter[100000], Epoch[000100], learning rate : 0.000300252, Train Loss: 0.144409955, Test MRAE: 0.177213684, Test RMSE: 0.025560830, Test PSNR: 33.900306702
2022-04-04 06:28:11 - Iter[101000], Epoch[000101], learning rate : 0.000298437, Train Loss: 0.146764740, Test MRAE: 0.209297180, Test RMSE: 0.028298480, Test PSNR: 32.552898407
2022-04-04 06:43:17 - Iter[102000], Epoch[000102], learning rate : 0.000296612, Train Loss: 0.147246465, Test MRAE: 0.190277949, Test RMSE: 0.026472179, Test PSNR: 33.188842773
2022-04-04 06:58:22 - Iter[103000], Epoch[000103], learning rate : 0.000294776, Train Loss: 0.146581918, Test MRAE: 0.197683260, Test RMSE: 0.027626824, Test PSNR: 32.783699036
2022-04-04 07:13:28 - Iter[104000], Epoch[000104], learning rate : 0.000292929, Train Loss: 0.146742970, Test MRAE: 0.191199228, Test RMSE: 0.026753768, Test PSNR: 33.263164520
2022-04-04 07:28:33 - Iter[105000], Epoch[000105], learning rate : 0.000291073, Train Loss: 0.146190017, Test MRAE: 0.209828049, Test RMSE: 0.028815746, Test PSNR: 32.542137146
2022-04-04 07:43:38 - Iter[106000], Epoch[000106], learning rate : 0.000289207, Train Loss: 0.146303505, Test MRAE: 0.203425631, Test RMSE: 0.028255586, Test PSNR: 32.893619537
2022-04-04 07:58:42 - Iter[107000], Epoch[000107], learning rate : 0.000287330, Train Loss: 0.145939469, Test MRAE: 0.205534205, Test RMSE: 0.028100140, Test PSNR: 32.720920563
2022-04-04 08:13:45 - Iter[108000], Epoch[000108], learning rate : 0.000285445, Train Loss: 0.145626575, Test MRAE: 0.199075297, Test RMSE: 0.027747070, Test PSNR: 32.628967285
2022-04-04 08:28:49 - Iter[109000], Epoch[000109], learning rate : 0.000283550, Train Loss: 0.145179436, Test MRAE: 0.197858781, Test RMSE: 0.027689554, Test PSNR: 32.944408417
2022-04-04 08:43:53 - Iter[110000], Epoch[000110], learning rate : 0.000281646, Train Loss: 0.144860938, Test MRAE: 0.192567900, Test RMSE: 0.027259275, Test PSNR: 33.077899933
2022-04-04 08:58:57 - Iter[111000], Epoch[000111], learning rate : 0.000279733, Train Loss: 0.144714609, Test MRAE: 0.204205051, Test RMSE: 0.029084271, Test PSNR: 32.770385742
2022-04-04 09:14:02 - Iter[112000], Epoch[000112], learning rate : 0.000277811, Train Loss: 0.144318491, Test MRAE: 0.223211572, Test RMSE: 0.031434882, Test PSNR: 31.881069183
2022-04-04 09:29:06 - Iter[113000], Epoch[000113], learning rate : 0.000275881, Train Loss: 0.144020408, Test MRAE: 0.194831327, Test RMSE: 0.026703479, Test PSNR: 33.438613892
2022-04-04 09:44:12 - Iter[114000], Epoch[000114], learning rate : 0.000273943, Train Loss: 0.143576324, Test MRAE: 0.203750417, Test RMSE: 0.028259305, Test PSNR: 32.888893127
2022-04-04 09:59:17 - Iter[115000], Epoch[000115], learning rate : 0.000271996, Train Loss: 0.143247142, Test MRAE: 0.198985398, Test RMSE: 0.027793799, Test PSNR: 32.779857635
2022-04-04 10:14:21 - Iter[116000], Epoch[000116], learning rate : 0.000270042, Train Loss: 0.143120974, Test MRAE: 0.207176000, Test RMSE: 0.030347060, Test PSNR: 32.556030273
2022-04-04 10:29:25 - Iter[117000], Epoch[000117], learning rate : 0.000268080, Train Loss: 0.142650396, Test MRAE: 0.187940046, Test RMSE: 0.025674313, Test PSNR: 33.605190277
2022-04-04 10:44:29 - Iter[118000], Epoch[000118], learning rate : 0.000266111, Train Loss: 0.142707929, Test MRAE: 0.230560794, Test RMSE: 0.033057593, Test PSNR: 31.559970856
2022-04-04 10:59:33 - Iter[119000], Epoch[000119], learning rate : 0.000264134, Train Loss: 0.142307356, Test MRAE: 0.191931799, Test RMSE: 0.026989702, Test PSNR: 33.181324005
2022-04-04 11:14:37 - Iter[120000], Epoch[000120], learning rate : 0.000262151, Train Loss: 0.141990557, Test MRAE: 0.192476153, Test RMSE: 0.026124485, Test PSNR: 33.420986176
2022-04-04 11:29:41 - Iter[121000], Epoch[000121], learning rate : 0.000260161, Train Loss: 0.141687348, Test MRAE: 0.184224799, Test RMSE: 0.025840133, Test PSNR: 33.450706482
2022-04-04 11:44:44 - Iter[122000], Epoch[000122], learning rate : 0.000258164, Train Loss: 0.141496018, Test MRAE: 0.211093605, Test RMSE: 0.030541541, Test PSNR: 32.220798492
2022-04-04 11:59:47 - Iter[123000], Epoch[000123], learning rate : 0.000256161, Train Loss: 0.141279995, Test MRAE: 0.211332962, Test RMSE: 0.031496756, Test PSNR: 32.452415466
2022-04-04 12:14:50 - Iter[124000], Epoch[000124], learning rate : 0.000254152, Train Loss: 0.140935808, Test MRAE: 0.182014048, Test RMSE: 0.026095385, Test PSNR: 33.434387207
2022-04-04 12:29:53 - Iter[125000], Epoch[000125], learning rate : 0.000252136, Train Loss: 0.140553787, Test MRAE: 0.201630816, Test RMSE: 0.028498441, Test PSNR: 32.941055298
2022-04-04 12:44:57 - Iter[126000], Epoch[000126], learning rate : 0.000250116, Train Loss: 0.140210062, Test MRAE: 0.196667418, Test RMSE: 0.027533025, Test PSNR: 33.197334290
2022-04-04 13:00:01 - Iter[127000], Epoch[000127], learning rate : 0.000248089, Train Loss: 0.139874682, Test MRAE: 0.193314314, Test RMSE: 0.026069442, Test PSNR: 33.322265625
2022-04-04 13:15:05 - Iter[128000], Epoch[000128], learning rate : 0.000246058, Train Loss: 0.139610812, Test MRAE: 0.184793472, Test RMSE: 0.026267387, Test PSNR: 33.467800140
2022-04-04 13:30:11 - Iter[129000], Epoch[000129], learning rate : 0.000244022, Train Loss: 0.139311388, Test MRAE: 0.181860313, Test RMSE: 0.025679292, Test PSNR: 33.517444611
2022-04-04 13:45:16 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.138979465, Test MRAE: 0.192421168, Test RMSE: 0.025989801, Test PSNR: 33.328197479
2022-04-04 14:00:22 - Iter[131000], Epoch[000131], learning rate : 0.000239935, Train Loss: 0.138704389, Test MRAE: 0.189146712, Test RMSE: 0.027482571, Test PSNR: 33.170711517
2022-04-04 14:15:29 - Iter[132000], Epoch[000132], learning rate : 0.000237885, Train Loss: 0.138448209, Test MRAE: 0.244230568, Test RMSE: 0.033546329, Test PSNR: 31.520547867
2022-04-04 14:30:55 - Iter[133000], Epoch[000133], learning rate : 0.000235830, Train Loss: 0.138437390, Test MRAE: 0.209491417, Test RMSE: 0.029441988, Test PSNR: 32.567466736
2022-04-04 14:45:59 - Iter[134000], Epoch[000134], learning rate : 0.000233772, Train Loss: 0.138078228, Test MRAE: 0.198247179, Test RMSE: 0.027585713, Test PSNR: 33.019546509
2022-04-04 15:01:04 - Iter[135000], Epoch[000135], learning rate : 0.000231711, Train Loss: 0.137766331, Test MRAE: 0.206885740, Test RMSE: 0.028574284, Test PSNR: 32.568927765
2022-04-04 15:16:07 - Iter[136000], Epoch[000136], learning rate : 0.000229646, Train Loss: 0.137506545, Test MRAE: 0.206970766, Test RMSE: 0.029319102, Test PSNR: 32.537002563
2022-04-04 15:31:11 - Iter[137000], Epoch[000137], learning rate : 0.000227577, Train Loss: 0.137159929, Test MRAE: 0.209170625, Test RMSE: 0.027991589, Test PSNR: 32.796226501
2022-04-04 15:46:15 - Iter[138000], Epoch[000138], learning rate : 0.000225506, Train Loss: 0.136870012, Test MRAE: 0.195187017, Test RMSE: 0.027397873, Test PSNR: 33.101245880
2022-04-04 16:01:18 - Iter[139000], Epoch[000139], learning rate : 0.000223432, Train Loss: 0.136604175, Test MRAE: 0.199766427, Test RMSE: 0.027963979, Test PSNR: 32.641944885
2022-04-04 16:16:22 - Iter[140000], Epoch[000140], learning rate : 0.000221356, Train Loss: 0.136273965, Test MRAE: 0.194659099, Test RMSE: 0.026631154, Test PSNR: 33.209129333
2022-04-04 16:31:26 - Iter[141000], Epoch[000141], learning rate : 0.000219277, Train Loss: 0.135969296, Test MRAE: 0.190013424, Test RMSE: 0.026332738, Test PSNR: 33.571544647
2022-04-04 16:46:29 - Iter[142000], Epoch[000142], learning rate : 0.000217196, Train Loss: 0.135687053, Test MRAE: 0.192097202, Test RMSE: 0.027293472, Test PSNR: 33.367752075
2022-04-04 17:01:33 - Iter[143000], Epoch[000143], learning rate : 0.000215113, Train Loss: 0.135427132, Test MRAE: 0.197036669, Test RMSE: 0.027829580, Test PSNR: 32.993965149
2022-04-04 17:16:38 - Iter[144000], Epoch[000144], learning rate : 0.000213029, Train Loss: 0.135131747, Test MRAE: 0.188829035, Test RMSE: 0.027458882, Test PSNR: 33.333042145
2022-04-04 17:31:44 - Iter[145000], Epoch[000145], learning rate : 0.000210943, Train Loss: 0.134840518, Test MRAE: 0.181015983, Test RMSE: 0.026195318, Test PSNR: 33.750247955
2022-04-04 17:46:50 - Iter[146000], Epoch[000146], learning rate : 0.000208856, Train Loss: 0.134597093, Test MRAE: 0.200263113, Test RMSE: 0.029295797, Test PSNR: 32.595726013
2022-04-04 18:01:56 - Iter[147000], Epoch[000147], learning rate : 0.000206769, Train Loss: 0.134317964, Test MRAE: 0.187696800, Test RMSE: 0.027629156, Test PSNR: 33.144210815
2022-04-04 18:17:03 - Iter[148000], Epoch[000148], learning rate : 0.000204680, Train Loss: 0.134244367, Test MRAE: 0.204239339, Test RMSE: 0.027791146, Test PSNR: 32.739940643
2022-04-04 18:32:09 - Iter[149000], Epoch[000149], learning rate : 0.000202591, Train Loss: 0.134082079, Test MRAE: 0.184530795, Test RMSE: 0.026201779, Test PSNR: 33.368007660
2022-04-04 18:47:13 - Iter[150000], Epoch[000150], learning rate : 0.000200502, Train Loss: 0.133785993, Test MRAE: 0.209925652, Test RMSE: 0.029662440, Test PSNR: 32.473392487
2022-04-04 19:02:18 - Iter[151000], Epoch[000151], learning rate : 0.000198413, Train Loss: 0.133535504, Test MRAE: 0.201949567, Test RMSE: 0.027838936, Test PSNR: 32.831115723
2022-04-04 19:17:22 - Iter[152000], Epoch[000152], learning rate : 0.000196324, Train Loss: 0.133240014, Test MRAE: 0.193777785, Test RMSE: 0.027263910, Test PSNR: 33.060874939
2022-04-04 19:32:25 - Iter[153000], Epoch[000153], learning rate : 0.000194236, Train Loss: 0.132989094, Test MRAE: 0.198152080, Test RMSE: 0.027015921, Test PSNR: 32.986412048
2022-04-04 19:47:29 - Iter[154000], Epoch[000154], learning rate : 0.000192148, Train Loss: 0.132727176, Test MRAE: 0.198437184, Test RMSE: 0.029202158, Test PSNR: 32.735092163
2022-04-04 20:02:33 - Iter[155000], Epoch[000155], learning rate : 0.000190061, Train Loss: 0.132450163, Test MRAE: 0.188878804, Test RMSE: 0.025823437, Test PSNR: 33.466331482
2022-04-04 20:17:36 - Iter[156000], Epoch[000156], learning rate : 0.000187975, Train Loss: 0.132193238, Test MRAE: 0.203520939, Test RMSE: 0.029164169, Test PSNR: 32.705257416
2022-04-04 20:32:40 - Iter[157000], Epoch[000157], learning rate : 0.000185891, Train Loss: 0.131925568, Test MRAE: 0.203883335, Test RMSE: 0.028785149, Test PSNR: 32.828910828
2022-04-04 20:47:44 - Iter[158000], Epoch[000158], learning rate : 0.000183808, Train Loss: 0.131660596, Test MRAE: 0.179988310, Test RMSE: 0.025881417, Test PSNR: 33.442588806

xcyquan · Answer 2 · Sun May 08 2022 21:14:14 GMT+0800 (China Standard Time)

非常感谢您的回复，还要麻烦您debug一下我们的模型，已发您qq邮箱！

Yuanhao Cai · Answer 3 · Sun May 08 2022 21:56:29 GMT+0800 (China Standard Time)

你好，我们用你的预训练模型测出来的结果如下
MRAE:0.20801089704036713, RMSE: 0.029703451320528984, PNSR:32.64384460449219
很正常，你可以再自己检查一下

你可以用这个命令测一下你的预训练模型
cd /MST-plus-plus/test_develop_code/
python test.py --data_root ../dataset/ --method mst --pretrained_model_path 你的预训练模型路径 --outf ./exp/mst/ --gpu_id 0

或者你用我们的预训练模型，运行如下命令
cd /MST-plus-plus/test_develop_code/
python test.py --data_root ../dataset/ --method mst --pretrained_model_path ./model_zoo/mst.pth --outf ./exp/mst/ --gpu_id 0

xcyquan · Answer 4 · Sun May 08 2022 22:28:21 GMT+0800 (China Standard Time)

您好，这个是我复现的别的模型
load model from /home/a6000/code/MST-plus-plus-master/train_code/exp/restormer/2022_05_01_23_46_16/net_395epoch.pth

method:restormer, mrae:0.21244066953659058, rmse:0.02941896766424179, psnr:33.00621795654297

这是用您开源的MST跑出来的结果
load model from /home/a6000/code/MST-plus-plus-master/train_code/exp/mst.pth
method:mst, mrae:0.17721526324748993, rmse:0.02556183747947216, psnr:19.032461166381836
这是我复现的MST跑出来的
load model from /home/a6000/code/MST-plus-plus-master/train_code/exp/mst/2022_04_23_09_31_08/net_395epoch.pth
method:mst, mrae:0.20802144706249237, rmse:0.02970574051141739, psnr:19.075653076171875

不知道哪里出了问题。。。

Yuanhao Cai · Answer 5 · Sun May 08 2022 22:36:30 GMT+0800 (China Standard Time)

你这个MRAE和RMSE都对的呀，为啥PSNR不对呢？PSNR和RMSE都是基于MSE算出来的。

你有按照我们给的环境去配吗？

xcyquan · Answer 6 · Sun May 08 2022 23:37:34 GMT+0800 (China Standard Time)

你这个MRAE和RMSE都对的呀，为啥PSNR不对呢？PSNR和RMSE都是基于MSE算出来的。

你有按照我们给的环境去配吗？
主要我别的模型能跑出相应的指标，您的MST-PLUS-PLUS能拍出正确的指标

Yuanhao Cai · Answer 7 · Sun May 08 2022 23:41:54 GMT+0800 (China Standard Time)

这就很玄幻了，我们测也正常，训也正常，其他人用也正常。
你看你再检查一下吧。理论上RMSE对上了，PSNR就一定对上。
你可以在test.py里面插断点debug一下。

linjing7 · Answer 8 · Mon May 09 2022 00:20:59 GMT+0800 (China Standard Time)

你好，我认为可能是pytorch版本不一致导致的，在Loss_PSNR( )的实现中，使用了一些torch.nn的内置函数，可能不同版本对一些异常值的处理方式不同。你可以：

改一下pytorch的版本，我们试过torch==1.7.0和1.8.0都可以
或者换一种不需要pytorch，只使用numpy和math的计算psnr的方法：
(1)先将以下代码复制到test_develop_code/utils.py中：

import math
def calc_psnr(img1, img2, data_range=255):
    img1 = img1.clamp(0., 1.).mul_(data_range).cpu().numpy()
    img2 = img2.clamp(0., 1.).mul_(data_range).cpu().numpy()
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2)**2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))

(2)再将test_develop_code/test.py的第54行~69行替换为以下代码，然后再重新测一遍

from utils import calc_psnr
with torch.no_grad():
    # compute output
    if method=='awan':   # To avoid out of memory, we crop the center region as input for AWAN.
        output = model(input[:, :, 118:-118, 118:-118])
        loss_mrae = criterion_mrae(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
        loss_rmse = criterion_rmse(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
        loss_psnr = calc_psnr(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
    else:
        output = model(input)
        loss_mrae = criterion_mrae(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
        loss_rmse = criterion_rmse(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
        loss_psnr = calc_psnr(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
# record loss
losses_mrae.update(loss_mrae.data)
losses_rmse.update(loss_rmse.data)
losses_psnr.update(loss_psnr)

希望对你有帮助~

xcyquan · Answer 9 · Mon May 09 2022 16:16:28 GMT+0800 (China Standard Time)

你好，我认为可能是pytorch版本不一致导致的，在Loss_PSNR( )的实现中，使用了一些torch.nn的内置函数，可能不同版本对一些异常值的处理方式不同。你可以：

改一下pytorch的版本，我们试过torch==1.7.0和1.8.0都可以

或者换一种不需要pytorch，只使用numpy和math的计算psnr的方法：
(1)先将以下代码复制到test_develop_code/utils.py中：
import math
def calc_psnr(img1, img2, data_range=255):
    img1 = img1.clamp(0., 1.).mul_(data_range).cpu().numpy()
    img2 = img2.clamp(0., 1.).mul_(data_range).cpu().numpy()
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2)**2)
    if mse == 0:
        return float('inf')
    return 20 * math.log10(255.0 / math.sqrt(mse))
(2)再将test_develop_code/test.py的第54行~69行替换为以下代码，然后再重新测一遍
from utils import calc_psnr
with torch.no_grad():
    # compute output
    if method=='awan':   # To avoid out of memory, we crop the center region as input for AWAN.
        output = model(input[:, :, 118:-118, 118:-118])
        loss_mrae = criterion_mrae(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
        loss_rmse = criterion_rmse(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
        loss_psnr = calc_psnr(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
    else:
        output = model(input)
        loss_mrae = criterion_mrae(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
        loss_rmse = criterion_rmse(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
        loss_psnr = calc_psnr(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
# record loss
losses_mrae.update(loss_mrae.data)
losses_rmse.update(loss_rmse.data)
losses_psnr.update(loss_psnr)
希望对你有帮助~
哇，不用pytorch能跑出正常的指标，就很神奇。。。主要我别的模型的指标是正常，就很玄幻！感谢您的耐心帮助！

xcyquan · Answer 10 · Mon May 09 2022 16:17:37 GMT+0800 (China Standard Time)

这就很玄幻了，我们测也正常，训也正常，其他人用也正常。你看你再检查一下吧。理论上RMSE对上了，PSNR就一定对上。你可以在test.py里面插断点debug一下。

感谢大佬的帮助，就很玄幻，现在可以了，使用numpy就可以！您人非常好，祝您科研顺利！