MST++的PSNR34.32是ensembel 以后的结果吗？
chenjiachengzzz opened this issue · comments
作者您好，您的工作非常棒，令人激动!我有三个疑问，希望您能解答：
1）MST++的PSNR34.32是ensembel 以后的结果吗
2）train了300个epoch以后，是使用最后的pth来进行计算指标吗，还是根据测试集上的指标来选一个最好的模型
3）我按照您设置的参数进行训练，不知为何train了395个epoch...
训练命令（一张3090显卡）：python train.py --method mst_plus_plus --batch_size 20 --end_epoch 300 --init_lr 4e-4 --outf ./exp/mst_plus_plus/ --patch_size 128 --stride 8 --gpu_id 0
[iter:395460/300000],lr=0.000092650,train_losses.avg=0.102350816
[iter:395480/300000],lr=0.000092685,train_losses.avg=0.102352098
[iter:395500/300000],lr=0.000092720,train_losses.avg=0.102352992
[iter:395520/300000],lr=0.000092755,train_losses.avg=0.102354914
[iter:395540/300000],lr=0.000092791,train_losses.avg=0.102356508
[iter:395560/300000],lr=0.000092826,train_losses.avg=0.102357790
然后，我用最后的模型跑出来的指标为：
load model from ../train_code/exp/mst_plus_plus/2022_04_23_14_46_46/net_395epoch.pth
method:mst_plus_plus, mrae:0.2037583440542221, rmse:0.029374651610851288, psnr:32.78853988647461
我感觉和您文中的指标还有一定差距，您觉得这正常吗，或者是有什么原因造成的（有别的epoch能跑到33.6左右的指标）
希望能得到您的解答！
您好
1）MST++的34.32 dB是单模型的效果
2) 取MRAE最低的模型
3）训练395个epoch是为了将数据完整地遍历四次
我们刚刚check了一遍，我们的训练代码没问题，提供的预训练模型也能达到这个指标。
分析造成这个原因为
1）数据里面有一些数据存在问题，他们为：
ARAD_1K_0314：原先的mat文件损坏，我们之前是把它去掉，不过数据集官方更换了一个mat文件。
ARAD_1K_0340：mat文件存在负数
ARAD_1K_0531：mat文件存在负数
2）训练误差导致，我们之前打比赛也遇见同样的情形，就是同一个模型相同的setting去训练好几次，得到的结果差距也较大，分析可能是数据集本身存在问题。
3）这里提供我们当时达到34.32dB 指标的完整 train log，我们取的是224epoch，你也可以把log发给我们看看
2022-04-01 17:34:48 - Iter[001000], Epoch[000001], learning rate : 0.000399989, Train Loss: 0.584950149, Test MRAE: 0.505284786, Test RMSE: 0.082308508, Test PSNR: 24.465768814
2022-04-01 17:41:53 - Iter[002000], Epoch[000002], learning rate : 0.000399956, Train Loss: 0.536539793, Test MRAE: 0.383451462, Test RMSE: 0.066276357, Test PSNR: 26.333047867
2022-04-01 17:48:58 - Iter[003000], Epoch[000003], learning rate : 0.000399902, Train Loss: 0.510977268, Test MRAE: 0.396951884, Test RMSE: 0.060446210, Test PSNR: 26.629571915
2022-04-01 17:56:03 - Iter[004000], Epoch[000004], learning rate : 0.000399825, Train Loss: 0.496563166, Test MRAE: 0.354414284, Test RMSE: 0.057640508, Test PSNR: 27.368322372
2022-04-01 18:03:08 - Iter[005000], Epoch[000005], learning rate : 0.000399727, Train Loss: 0.486273319, Test MRAE: 0.348487437, Test RMSE: 0.059620392, Test PSNR: 27.291925430
2022-04-01 18:10:13 - Iter[006000], Epoch[000006], learning rate : 0.000399606, Train Loss: 0.478124112, Test MRAE: 0.333887249, Test RMSE: 0.053735230, Test PSNR: 27.781486511
2022-04-01 18:17:17 - Iter[007000], Epoch[000007], learning rate : 0.000399464, Train Loss: 0.471671075, Test MRAE: 0.444502473, Test RMSE: 0.078787535, Test PSNR: 25.333122253
2022-04-01 18:24:22 - Iter[008000], Epoch[000008], learning rate : 0.000399301, Train Loss: 0.465782464, Test MRAE: 0.363852650, Test RMSE: 0.057836063, Test PSNR: 27.088405609
2022-04-01 18:31:27 - Iter[009000], Epoch[000009], learning rate : 0.000399115, Train Loss: 0.460375816, Test MRAE: 0.335046530, Test RMSE: 0.056397218, Test PSNR: 27.647294998
2022-04-01 18:38:32 - Iter[010000], Epoch[000010], learning rate : 0.000398907, Train Loss: 0.455432057, Test MRAE: 0.406955987, Test RMSE: 0.068730623, Test PSNR: 26.183654785
2022-04-01 18:45:37 - Iter[011000], Epoch[000011], learning rate : 0.000398678, Train Loss: 0.451046467, Test MRAE: 0.341477811, Test RMSE: 0.055606693, Test PSNR: 27.462575912
2022-04-01 18:52:41 - Iter[012000], Epoch[000012], learning rate : 0.000398427, Train Loss: 0.446595907, Test MRAE: 0.353364170, Test RMSE: 0.057747085, Test PSNR: 27.494455338
2022-04-01 18:59:46 - Iter[013000], Epoch[000013], learning rate : 0.000398154, Train Loss: 0.442328036, Test MRAE: 0.350452334, Test RMSE: 0.056124873, Test PSNR: 27.861591339
2022-04-01 19:06:50 - Iter[014000], Epoch[000014], learning rate : 0.000397860, Train Loss: 0.437750310, Test MRAE: 0.299767703, Test RMSE: 0.046758004, Test PSNR: 29.536672592
2022-04-01 19:13:55 - Iter[015000], Epoch[000015], learning rate : 0.000397544, Train Loss: 0.432701677, Test MRAE: 0.361019135, Test RMSE: 0.055260185, Test PSNR: 27.696432114
2022-04-01 19:21:00 - Iter[016000], Epoch[000016], learning rate : 0.000397207, Train Loss: 0.427369386, Test MRAE: 0.348699480, Test RMSE: 0.051816467, Test PSNR: 28.300346375
2022-04-01 19:28:04 - Iter[017000], Epoch[000017], learning rate : 0.000396847, Train Loss: 0.421481818, Test MRAE: 0.288097441, Test RMSE: 0.045858938, Test PSNR: 29.549434662
2022-04-01 19:35:09 - Iter[018000], Epoch[000018], learning rate : 0.000396467, Train Loss: 0.415746957, Test MRAE: 0.244325474, Test RMSE: 0.034505930, Test PSNR: 30.941785812
2022-04-01 19:42:14 - Iter[019000], Epoch[000019], learning rate : 0.000396065, Train Loss: 0.409877777, Test MRAE: 0.315324932, Test RMSE: 0.048803244, Test PSNR: 28.417842865
2022-04-01 19:49:18 - Iter[020000], Epoch[000020], learning rate : 0.000395641, Train Loss: 0.404460698, Test MRAE: 0.332354933, Test RMSE: 0.049890943, Test PSNR: 28.136251450
2022-04-01 19:56:23 - Iter[021000], Epoch[000021], learning rate : 0.000395196, Train Loss: 0.399340391, Test MRAE: 0.245741591, Test RMSE: 0.039041657, Test PSNR: 30.708288193
2022-04-01 20:03:27 - Iter[022000], Epoch[000022], learning rate : 0.000394729, Train Loss: 0.394383997, Test MRAE: 0.263525605, Test RMSE: 0.038467873, Test PSNR: 30.458463669
2022-04-01 20:10:32 - Iter[023000], Epoch[000023], learning rate : 0.000394242, Train Loss: 0.389635682, Test MRAE: 0.244765565, Test RMSE: 0.038990323, Test PSNR: 30.396646500
2022-04-01 20:17:37 - Iter[024000], Epoch[000024], learning rate : 0.000393733, Train Loss: 0.385013252, Test MRAE: 0.270854801, Test RMSE: 0.043596063, Test PSNR: 29.211574554
2022-04-01 20:24:42 - Iter[025000], Epoch[000025], learning rate : 0.000393203, Train Loss: 0.380483001, Test MRAE: 0.294368446, Test RMSE: 0.043374084, Test PSNR: 28.909912109
2022-04-01 20:31:47 - Iter[026000], Epoch[000026], learning rate : 0.000392651, Train Loss: 0.376212955, Test MRAE: 0.244931385, Test RMSE: 0.036789756, Test PSNR: 30.863887787
2022-04-01 20:38:52 - Iter[027000], Epoch[000027], learning rate : 0.000392079, Train Loss: 0.372227609, Test MRAE: 0.259893537, Test RMSE: 0.041794285, Test PSNR: 30.255331039
2022-04-01 20:45:56 - Iter[028000], Epoch[000028], learning rate : 0.000391486, Train Loss: 0.368390054, Test MRAE: 0.247782648, Test RMSE: 0.041366719, Test PSNR: 29.833391190
2022-04-01 20:53:02 - Iter[029000], Epoch[000029], learning rate : 0.000390872, Train Loss: 0.364704400, Test MRAE: 0.233007267, Test RMSE: 0.035965577, Test PSNR: 31.122755051
2022-04-01 21:00:07 - Iter[030000], Epoch[000030], learning rate : 0.000390236, Train Loss: 0.361103654, Test MRAE: 0.204645157, Test RMSE: 0.030946571, Test PSNR: 32.256484985
2022-04-01 21:07:12 - Iter[031000], Epoch[000031], learning rate : 0.000389580, Train Loss: 0.357783496, Test MRAE: 0.241499379, Test RMSE: 0.037305184, Test PSNR: 30.882730484
2022-04-01 21:14:17 - Iter[032000], Epoch[000032], learning rate : 0.000388904, Train Loss: 0.354456693, Test MRAE: 0.218291149, Test RMSE: 0.031212687, Test PSNR: 32.413223267
2022-04-01 21:21:23 - Iter[033000], Epoch[000033], learning rate : 0.000388206, Train Loss: 0.351404607, Test MRAE: 0.255799562, Test RMSE: 0.039805494, Test PSNR: 30.379692078
2022-04-01 21:28:28 - Iter[034000], Epoch[000034], learning rate : 0.000387488, Train Loss: 0.348452777, Test MRAE: 0.229405567, Test RMSE: 0.033707343, Test PSNR: 31.395647049
2022-04-01 21:35:33 - Iter[035000], Epoch[000035], learning rate : 0.000386750, Train Loss: 0.345589787, Test MRAE: 0.228498265, Test RMSE: 0.033607174, Test PSNR: 31.477565765
2022-04-01 21:42:38 - Iter[036000], Epoch[000036], learning rate : 0.000385991, Train Loss: 0.342689037, Test MRAE: 0.226025045, Test RMSE: 0.035209049, Test PSNR: 31.385534286
2022-04-01 21:49:43 - Iter[037000], Epoch[000037], learning rate : 0.000385212, Train Loss: 0.340007842, Test MRAE: 0.205528080, Test RMSE: 0.030785879, Test PSNR: 32.313484192
2022-04-01 21:56:48 - Iter[038000], Epoch[000038], learning rate : 0.000384413, Train Loss: 0.337432981, Test MRAE: 0.242565155, Test RMSE: 0.035048563, Test PSNR: 31.228994370
2022-04-01 22:03:53 - Iter[039000], Epoch[000039], learning rate : 0.000383593, Train Loss: 0.334808648, Test MRAE: 0.225129545, Test RMSE: 0.036084954, Test PSNR: 31.080299377
2022-04-01 22:10:58 - Iter[040000], Epoch[000040], learning rate : 0.000382753, Train Loss: 0.332305431, Test MRAE: 0.209638357, Test RMSE: 0.031819548, Test PSNR: 31.867336273
2022-04-01 22:18:03 - Iter[041000], Epoch[000041], learning rate : 0.000381893, Train Loss: 0.329856128, Test MRAE: 0.243082359, Test RMSE: 0.037743066, Test PSNR: 30.564802170
2022-04-01 22:25:08 - Iter[042000], Epoch[000042], learning rate : 0.000381014, Train Loss: 0.327482730, Test MRAE: 0.222068936, Test RMSE: 0.032870341, Test PSNR: 31.703283310
2022-04-01 22:32:12 - Iter[043000], Epoch[000043], learning rate : 0.000380115, Train Loss: 0.325240016, Test MRAE: 0.229235604, Test RMSE: 0.034286030, Test PSNR: 31.370164871
2022-04-01 22:39:17 - Iter[044000], Epoch[000044], learning rate : 0.000379195, Train Loss: 0.323110878, Test MRAE: 0.213927716, Test RMSE: 0.033160798, Test PSNR: 31.717891693
2022-04-01 22:46:22 - Iter[045000], Epoch[000045], learning rate : 0.000378257, Train Loss: 0.321074039, Test MRAE: 0.207274720, Test RMSE: 0.030418370, Test PSNR: 32.235691071
2022-04-01 22:53:28 - Iter[046000], Epoch[000046], learning rate : 0.000377299, Train Loss: 0.318956673, Test MRAE: 0.202121153, Test RMSE: 0.032019392, Test PSNR: 32.241413116
2022-04-01 23:00:33 - Iter[047000], Epoch[000047], learning rate : 0.000376321, Train Loss: 0.316959113, Test MRAE: 0.205194235, Test RMSE: 0.032787323, Test PSNR: 31.792476654
2022-04-01 23:07:38 - Iter[048000], Epoch[000048], learning rate : 0.000375324, Train Loss: 0.314950347, Test MRAE: 0.203845099, Test RMSE: 0.031617835, Test PSNR: 32.212875366
2022-04-01 23:14:43 - Iter[049000], Epoch[000049], learning rate : 0.000374308, Train Loss: 0.313015461, Test MRAE: 0.258322805, Test RMSE: 0.037195712, Test PSNR: 30.763568878
2022-04-01 23:21:48 - Iter[050000], Epoch[000050], learning rate : 0.000373273, Train Loss: 0.311186939, Test MRAE: 0.246989742, Test RMSE: 0.037638538, Test PSNR: 30.756891251
2022-04-01 23:28:54 - Iter[051000], Epoch[000051], learning rate : 0.000372219, Train Loss: 0.309284538, Test MRAE: 0.278562874, Test RMSE: 0.039878640, Test PSNR: 30.137786865
2022-04-01 23:35:59 - Iter[052000], Epoch[000052], learning rate : 0.000371146, Train Loss: 0.307502300, Test MRAE: 0.202273652, Test RMSE: 0.031961896, Test PSNR: 32.157718658
2022-04-01 23:43:04 - Iter[053000], Epoch[000053], learning rate : 0.000370055, Train Loss: 0.305666000, Test MRAE: 0.197266951, Test RMSE: 0.031423770, Test PSNR: 32.338787079
2022-04-01 23:50:09 - Iter[054000], Epoch[000054], learning rate : 0.000368945, Train Loss: 0.303980112, Test MRAE: 0.234829843, Test RMSE: 0.033855405, Test PSNR: 31.446868896
2022-04-01 23:57:15 - Iter[055000], Epoch[000055], learning rate : 0.000367816, Train Loss: 0.302361935, Test MRAE: 0.202010989, Test RMSE: 0.030435827, Test PSNR: 32.393886566
2022-04-02 00:04:19 - Iter[056000], Epoch[000056], learning rate : 0.000366669, Train Loss: 0.300656885, Test MRAE: 0.220728263, Test RMSE: 0.031705286, Test PSNR: 31.908329010
2022-04-02 00:11:24 - Iter[057000], Epoch[000057], learning rate : 0.000365504, Train Loss: 0.299047977, Test MRAE: 0.220085368, Test RMSE: 0.033380352, Test PSNR: 31.551395416
2022-04-02 00:18:29 - Iter[058000], Epoch[000058], learning rate : 0.000364320, Train Loss: 0.297482729, Test MRAE: 0.234033853, Test RMSE: 0.034875419, Test PSNR: 31.446674347
2022-04-02 00:25:34 - Iter[059000], Epoch[000059], learning rate : 0.000363119, Train Loss: 0.295996279, Test MRAE: 0.260196418, Test RMSE: 0.037792873, Test PSNR: 30.270656586
2022-04-02 00:32:39 - Iter[060000], Epoch[000060], learning rate : 0.000361900, Train Loss: 0.294436306, Test MRAE: 0.200080410, Test RMSE: 0.031053411, Test PSNR: 32.604763031
2022-04-02 00:39:44 - Iter[061000], Epoch[000061], learning rate : 0.000360663, Train Loss: 0.293001026, Test MRAE: 0.200418055, Test RMSE: 0.030117847, Test PSNR: 32.543945312
2022-04-02 00:46:49 - Iter[062000], Epoch[000062], learning rate : 0.000359409, Train Loss: 0.291474730, Test MRAE: 0.196053922, Test RMSE: 0.030740554, Test PSNR: 32.634941101
2022-04-02 00:53:54 - Iter[063000], Epoch[000063], learning rate : 0.000358137, Train Loss: 0.290053964, Test MRAE: 0.204541788, Test RMSE: 0.032342296, Test PSNR: 31.989198685
2022-04-02 01:00:59 - Iter[064000], Epoch[000064], learning rate : 0.000356848, Train Loss: 0.288649440, Test MRAE: 0.206324667, Test RMSE: 0.030595578, Test PSNR: 32.100811005
2022-04-02 01:08:04 - Iter[065000], Epoch[000065], learning rate : 0.000355542, Train Loss: 0.287254542, Test MRAE: 0.236757353, Test RMSE: 0.035118237, Test PSNR: 31.120126724
2022-04-02 01:15:09 - Iter[066000], Epoch[000066], learning rate : 0.000354219, Train Loss: 0.285909653, Test MRAE: 0.214723408, Test RMSE: 0.033630725, Test PSNR: 31.831535339
2022-04-02 01:22:14 - Iter[067000], Epoch[000067], learning rate : 0.000352879, Train Loss: 0.284521043, Test MRAE: 0.240211934, Test RMSE: 0.034098610, Test PSNR: 31.173513412
2022-04-02 01:29:18 - Iter[068000], Epoch[000068], learning rate : 0.000351522, Train Loss: 0.283179432, Test MRAE: 0.215387300, Test RMSE: 0.034216784, Test PSNR: 31.762895584
2022-04-02 01:36:23 - Iter[069000], Epoch[000069], learning rate : 0.000350149, Train Loss: 0.281881839, Test MRAE: 0.193218529, Test RMSE: 0.029353520, Test PSNR: 32.610298157
2022-04-02 01:43:28 - Iter[070000], Epoch[000070], learning rate : 0.000348759, Train Loss: 0.280629814, Test MRAE: 0.219208166, Test RMSE: 0.032447778, Test PSNR: 31.847682953
2022-04-02 01:50:33 - Iter[071000], Epoch[000071], learning rate : 0.000347353, Train Loss: 0.279351294, Test MRAE: 0.225306720, Test RMSE: 0.033141449, Test PSNR: 31.748260498
2022-04-02 01:57:38 - Iter[072000], Epoch[000072], learning rate : 0.000345931, Train Loss: 0.278063327, Test MRAE: 0.201316640, Test RMSE: 0.031217469, Test PSNR: 32.309852600
2022-04-02 02:04:43 - Iter[073000], Epoch[000073], learning rate : 0.000344493, Train Loss: 0.276834786, Test MRAE: 0.208189085, Test RMSE: 0.033277567, Test PSNR: 32.036525726
2022-04-02 02:11:48 - Iter[074000], Epoch[000074], learning rate : 0.000343039, Train Loss: 0.275610179, Test MRAE: 0.191424474, Test RMSE: 0.029514760, Test PSNR: 32.503597260
2022-04-02 02:18:53 - Iter[075000], Epoch[000075], learning rate : 0.000341569, Train Loss: 0.274460882, Test MRAE: 0.237475976, Test RMSE: 0.037835211, Test PSNR: 30.519735336
2022-04-02 02:25:58 - Iter[076000], Epoch[000076], learning rate : 0.000340084, Train Loss: 0.273309201, Test MRAE: 0.225662604, Test RMSE: 0.036369245, Test PSNR: 31.362052917
2022-04-02 02:33:03 - Iter[077000], Epoch[000077], learning rate : 0.000338584, Train Loss: 0.272117823, Test MRAE: 0.209062681, Test RMSE: 0.030369410, Test PSNR: 32.428474426
2022-04-02 02:40:09 - Iter[078000], Epoch[000078], learning rate : 0.000337069, Train Loss: 0.271009177, Test MRAE: 0.220699668, Test RMSE: 0.035450310, Test PSNR: 31.656976700
2022-04-02 02:47:14 - Iter[079000], Epoch[000079], learning rate : 0.000335538, Train Loss: 0.269896746, Test MRAE: 0.206833020, Test RMSE: 0.031051612, Test PSNR: 32.327026367
2022-04-02 02:54:19 - Iter[080000], Epoch[000080], learning rate : 0.000333993, Train Loss: 0.268783599, Test MRAE: 0.241886929, Test RMSE: 0.036845796, Test PSNR: 30.684600830
2022-04-02 03:01:24 - Iter[081000], Epoch[000081], learning rate : 0.000332433, Train Loss: 0.267670542, Test MRAE: 0.237772778, Test RMSE: 0.036397785, Test PSNR: 31.034585953
2022-04-02 03:08:29 - Iter[082000], Epoch[000082], learning rate : 0.000330859, Train Loss: 0.266590536, Test MRAE: 0.177056044, Test RMSE: 0.028156046, Test PSNR: 33.062782288
2022-04-02 03:15:34 - Iter[083000], Epoch[000083], learning rate : 0.000329270, Train Loss: 0.265530139, Test MRAE: 0.188389540, Test RMSE: 0.028906951, Test PSNR: 32.964668274
2022-04-02 03:22:39 - Iter[084000], Epoch[000084], learning rate : 0.000327668, Train Loss: 0.264473885, Test MRAE: 0.212135196, Test RMSE: 0.031424776, Test PSNR: 32.268173218
2022-04-02 03:29:44 - Iter[085000], Epoch[000085], learning rate : 0.000326051, Train Loss: 0.263511688, Test MRAE: 0.241904959, Test RMSE: 0.035207357, Test PSNR: 31.290084839
2022-04-02 03:36:49 - Iter[086000], Epoch[000086], learning rate : 0.000324421, Train Loss: 0.262477964, Test MRAE: 0.230033979, Test RMSE: 0.034003612, Test PSNR: 31.689195633
2022-04-02 03:43:54 - Iter[087000], Epoch[000087], learning rate : 0.000322777, Train Loss: 0.261422426, Test MRAE: 0.205279797, Test RMSE: 0.032579139, Test PSNR: 32.150108337
2022-04-02 03:50:59 - Iter[088000], Epoch[000088], learning rate : 0.000321119, Train Loss: 0.260446459, Test MRAE: 0.207475066, Test RMSE: 0.031879384, Test PSNR: 31.940074921
2022-04-02 03:58:04 - Iter[089000], Epoch[000089], learning rate : 0.000319449, Train Loss: 0.259436429, Test MRAE: 0.203695118, Test RMSE: 0.030128049, Test PSNR: 32.486568451
2022-04-02 04:05:09 - Iter[090000], Epoch[000090], learning rate : 0.000317765, Train Loss: 0.258483559, Test MRAE: 0.223796815, Test RMSE: 0.032803893, Test PSNR: 32.130069733
2022-04-02 04:12:14 - Iter[091000], Epoch[000091], learning rate : 0.000316068, Train Loss: 0.257490963, Test MRAE: 0.185592368, Test RMSE: 0.029418468, Test PSNR: 32.967449188
2022-04-02 04:19:19 - Iter[092000], Epoch[000092], learning rate : 0.000314359, Train Loss: 0.256523162, Test MRAE: 0.198010981, Test RMSE: 0.029994769, Test PSNR: 32.627872467
2022-04-02 04:26:24 - Iter[093000], Epoch[000093], learning rate : 0.000312637, Train Loss: 0.255578786, Test MRAE: 0.198814943, Test RMSE: 0.030847602, Test PSNR: 32.439441681
2022-04-02 04:33:28 - Iter[094000], Epoch[000094], learning rate : 0.000310903, Train Loss: 0.254646808, Test MRAE: 0.191866010, Test RMSE: 0.029148445, Test PSNR: 32.816082001
2022-04-02 04:40:33 - Iter[095000], Epoch[000095], learning rate : 0.000309157, Train Loss: 0.253726959, Test MRAE: 0.192979634, Test RMSE: 0.029738735, Test PSNR: 32.869152069
2022-04-02 04:47:38 - Iter[096000], Epoch[000096], learning rate : 0.000307399, Train Loss: 0.252818614, Test MRAE: 0.197973326, Test RMSE: 0.029848527, Test PSNR: 32.725940704
2022-04-02 04:54:43 - Iter[097000], Epoch[000097], learning rate : 0.000305629, Train Loss: 0.251925558, Test MRAE: 0.199427918, Test RMSE: 0.030138962, Test PSNR: 32.843128204
2022-04-02 05:01:48 - Iter[098000], Epoch[000098], learning rate : 0.000303848, Train Loss: 0.251030296, Test MRAE: 0.225085095, Test RMSE: 0.033624925, Test PSNR: 31.328903198
2022-04-02 05:08:54 - Iter[099000], Epoch[000099], learning rate : 0.000302056, Train Loss: 0.157932967, Test MRAE: 0.205710098, Test RMSE: 0.031232396, Test PSNR: 32.382919312
2022-04-02 05:15:59 - Iter[100000], Epoch[000100], learning rate : 0.000300252, Train Loss: 0.163398698, Test MRAE: 0.201674402, Test RMSE: 0.031359758, Test PSNR: 32.496139526
2022-04-02 05:23:04 - Iter[101000], Epoch[000101], learning rate : 0.000298437, Train Loss: 0.161681667, Test MRAE: 0.196461305, Test RMSE: 0.030521771, Test PSNR: 32.662445068
2022-04-02 05:30:09 - Iter[102000], Epoch[000102], learning rate : 0.000296612, Train Loss: 0.159914285, Test MRAE: 0.198964536, Test RMSE: 0.029838182, Test PSNR: 32.584655762
2022-04-02 05:37:15 - Iter[103000], Epoch[000103], learning rate : 0.000294776, Train Loss: 0.159850761, Test MRAE: 0.193117991, Test RMSE: 0.030304385, Test PSNR: 32.971481323
2022-04-02 05:44:20 - Iter[104000], Epoch[000104], learning rate : 0.000292929, Train Loss: 0.160278931, Test MRAE: 0.202768162, Test RMSE: 0.030321566, Test PSNR: 32.798717499
2022-04-02 05:51:25 - Iter[105000], Epoch[000105], learning rate : 0.000291073, Train Loss: 0.159916639, Test MRAE: 0.191225797, Test RMSE: 0.029187968, Test PSNR: 32.800048828
2022-04-02 05:58:31 - Iter[106000], Epoch[000106], learning rate : 0.000289207, Train Loss: 0.159715712, Test MRAE: 0.183520541, Test RMSE: 0.027756123, Test PSNR: 33.475131989
2022-04-02 06:05:36 - Iter[107000], Epoch[000107], learning rate : 0.000287330, Train Loss: 0.159149364, Test MRAE: 0.197204560, Test RMSE: 0.030854844, Test PSNR: 32.429656982
2022-04-02 06:12:41 - Iter[108000], Epoch[000108], learning rate : 0.000285445, Train Loss: 0.158860818, Test MRAE: 0.222721234, Test RMSE: 0.034804605, Test PSNR: 31.702642441
2022-04-02 06:19:46 - Iter[109000], Epoch[000109], learning rate : 0.000283550, Train Loss: 0.158561796, Test MRAE: 0.202080056, Test RMSE: 0.030551182, Test PSNR: 32.393035889
2022-04-02 06:26:51 - Iter[110000], Epoch[000110], learning rate : 0.000281646, Train Loss: 0.157757401, Test MRAE: 0.195683822, Test RMSE: 0.029755643, Test PSNR: 32.958679199
2022-04-02 06:33:57 - Iter[111000], Epoch[000111], learning rate : 0.000279733, Train Loss: 0.157292441, Test MRAE: 0.206986353, Test RMSE: 0.030402325, Test PSNR: 32.465286255
2022-04-02 06:41:02 - Iter[112000], Epoch[000112], learning rate : 0.000277811, Train Loss: 0.156894490, Test MRAE: 0.208183840, Test RMSE: 0.032357708, Test PSNR: 32.408733368
2022-04-02 06:48:07 - Iter[113000], Epoch[000113], learning rate : 0.000275881, Train Loss: 0.156429470, Test MRAE: 0.189887390, Test RMSE: 0.028604910, Test PSNR: 33.130912781
2022-04-02 06:55:11 - Iter[114000], Epoch[000114], learning rate : 0.000273943, Train Loss: 0.156084910, Test MRAE: 0.200650528, Test RMSE: 0.029752463, Test PSNR: 32.923568726
2022-04-02 07:02:16 - Iter[115000], Epoch[000115], learning rate : 0.000271996, Train Loss: 0.155678600, Test MRAE: 0.199261591, Test RMSE: 0.029610006, Test PSNR: 32.660301208
2022-04-02 07:09:21 - Iter[116000], Epoch[000116], learning rate : 0.000270042, Train Loss: 0.155197471, Test MRAE: 0.203930214, Test RMSE: 0.029894542, Test PSNR: 32.523139954
2022-04-02 07:16:27 - Iter[117000], Epoch[000117], learning rate : 0.000268080, Train Loss: 0.154847667, Test MRAE: 0.194434851, Test RMSE: 0.029162016, Test PSNR: 32.695648193
2022-04-02 07:23:32 - Iter[118000], Epoch[000118], learning rate : 0.000266111, Train Loss: 0.154292345, Test MRAE: 0.209756196, Test RMSE: 0.032150794, Test PSNR: 32.041221619
2022-04-02 07:30:37 - Iter[119000], Epoch[000119], learning rate : 0.000264134, Train Loss: 0.153992221, Test MRAE: 0.203500673, Test RMSE: 0.028459860, Test PSNR: 33.067180634
2022-04-02 07:37:42 - Iter[120000], Epoch[000120], learning rate : 0.000262151, Train Loss: 0.153527111, Test MRAE: 0.190151513, Test RMSE: 0.026965538, Test PSNR: 33.616230011
2022-04-02 07:44:47 - Iter[121000], Epoch[000121], learning rate : 0.000260161, Train Loss: 0.153072730, Test MRAE: 0.186896116, Test RMSE: 0.029284921, Test PSNR: 33.151531219
2022-04-02 07:51:52 - Iter[122000], Epoch[000122], learning rate : 0.000258164, Train Loss: 0.152724907, Test MRAE: 0.221988827, Test RMSE: 0.035273857, Test PSNR: 31.479328156
2022-04-02 07:58:57 - Iter[123000], Epoch[000123], learning rate : 0.000256161, Train Loss: 0.152476281, Test MRAE: 0.188700199, Test RMSE: 0.028302073, Test PSNR: 33.347724915
2022-04-02 08:06:02 - Iter[124000], Epoch[000124], learning rate : 0.000254152, Train Loss: 0.152080312, Test MRAE: 0.184164077, Test RMSE: 0.028316485, Test PSNR: 33.405300140
2022-04-02 08:13:07 - Iter[125000], Epoch[000125], learning rate : 0.000252136, Train Loss: 0.151791677, Test MRAE: 0.196370155, Test RMSE: 0.030690564, Test PSNR: 32.660198212
2022-04-02 08:20:12 - Iter[126000], Epoch[000126], learning rate : 0.000250116, Train Loss: 0.151405141, Test MRAE: 0.205891445, Test RMSE: 0.029957492, Test PSNR: 32.435657501
2022-04-02 08:27:18 - Iter[127000], Epoch[000127], learning rate : 0.000248089, Train Loss: 0.150942832, Test MRAE: 0.198621213, Test RMSE: 0.031169182, Test PSNR: 32.464572906
2022-04-02 08:34:23 - Iter[128000], Epoch[000128], learning rate : 0.000246058, Train Loss: 0.150578201, Test MRAE: 0.187796995, Test RMSE: 0.027740559, Test PSNR: 33.384307861
2022-04-02 08:41:28 - Iter[129000], Epoch[000129], learning rate : 0.000244022, Train Loss: 0.150222331, Test MRAE: 0.206655160, Test RMSE: 0.030705499, Test PSNR: 32.419868469
2022-04-02 08:48:33 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.149831668, Test MRAE: 0.205621853, Test RMSE: 0.030956969, Test PSNR: 32.388271332
2022-04-02 08:55:39 - Iter[131000], Epoch[000131], learning rate : 0.000239935, Train Loss: 0.149391443, Test MRAE: 0.208711639, Test RMSE: 0.031353723, Test PSNR: 32.363273621
2022-04-02 09:02:44 - Iter[132000], Epoch[000132], learning rate : 0.000237885, Train Loss: 0.149109617, Test MRAE: 0.199780196, Test RMSE: 0.029980421, Test PSNR: 32.739299774
2022-04-02 09:09:49 - Iter[133000], Epoch[000133], learning rate : 0.000235830, Train Loss: 0.148754209, Test MRAE: 0.209734231, Test RMSE: 0.031571966, Test PSNR: 32.233982086
2022-04-02 09:16:54 - Iter[134000], Epoch[000134], learning rate : 0.000233772, Train Loss: 0.148477510, Test MRAE: 0.200577706, Test RMSE: 0.029325893, Test PSNR: 32.766677856
2022-04-02 09:24:00 - Iter[135000], Epoch[000135], learning rate : 0.000231711, Train Loss: 0.148074359, Test MRAE: 0.201025933, Test RMSE: 0.028436789, Test PSNR: 33.106365204
2022-04-02 09:31:05 - Iter[136000], Epoch[000136], learning rate : 0.000229646, Train Loss: 0.147701040, Test MRAE: 0.191481620, Test RMSE: 0.027902594, Test PSNR: 33.028308868
2022-04-02 09:38:10 - Iter[137000], Epoch[000137], learning rate : 0.000227577, Train Loss: 0.147324309, Test MRAE: 0.195249930, Test RMSE: 0.028709780, Test PSNR: 32.955074310
2022-04-02 09:45:15 - Iter[138000], Epoch[000138], learning rate : 0.000225506, Train Loss: 0.146982267, Test MRAE: 0.177937388, Test RMSE: 0.024885396, Test PSNR: 34.174915314
2022-04-02 09:52:20 - Iter[139000], Epoch[000139], learning rate : 0.000223432, Train Loss: 0.146660596, Test MRAE: 0.182144701, Test RMSE: 0.027686400, Test PSNR: 33.420753479
2022-04-02 09:59:24 - Iter[140000], Epoch[000140], learning rate : 0.000221356, Train Loss: 0.146321699, Test MRAE: 0.203623220, Test RMSE: 0.031879637, Test PSNR: 32.165897369
2022-04-02 10:06:29 - Iter[141000], Epoch[000141], learning rate : 0.000219277, Train Loss: 0.145945460, Test MRAE: 0.193808928, Test RMSE: 0.027460031, Test PSNR: 33.376766205
2022-04-02 10:13:34 - Iter[142000], Epoch[000142], learning rate : 0.000217196, Train Loss: 0.145572960, Test MRAE: 0.183090851, Test RMSE: 0.026387624, Test PSNR: 33.609401703
2022-04-02 10:20:39 - Iter[143000], Epoch[000143], learning rate : 0.000215113, Train Loss: 0.145230576, Test MRAE: 0.198559508, Test RMSE: 0.029728927, Test PSNR: 32.738819122
2022-04-02 10:27:44 - Iter[144000], Epoch[000144], learning rate : 0.000213029, Train Loss: 0.144875452, Test MRAE: 0.184433520, Test RMSE: 0.025541564, Test PSNR: 33.760311127
2022-04-02 10:34:49 - Iter[145000], Epoch[000145], learning rate : 0.000210943, Train Loss: 0.144499391, Test MRAE: 0.189881548, Test RMSE: 0.029831864, Test PSNR: 32.937168121
2022-04-02 10:41:58 - Iter[146000], Epoch[000146], learning rate : 0.000208856, Train Loss: 0.144189715, Test MRAE: 0.186690629, Test RMSE: 0.028673708, Test PSNR: 32.953140259
2022-04-02 10:49:04 - Iter[147000], Epoch[000147], learning rate : 0.000206769, Train Loss: 0.143872991, Test MRAE: 0.183226421, Test RMSE: 0.028038390, Test PSNR: 32.986354828
2022-04-02 10:56:12 - Iter[148000], Epoch[000148], learning rate : 0.000204680, Train Loss: 0.143495172, Test MRAE: 0.201523677, Test RMSE: 0.029933879, Test PSNR: 32.555938721
2022-04-02 11:03:18 - Iter[149000], Epoch[000149], learning rate : 0.000202591, Train Loss: 0.143158302, Test MRAE: 0.197135419, Test RMSE: 0.029186174, Test PSNR: 32.945064545
2022-04-02 11:10:23 - Iter[150000], Epoch[000150], learning rate : 0.000200502, Train Loss: 0.142846838, Test MRAE: 0.189685300, Test RMSE: 0.027247800, Test PSNR: 33.393627167
2022-04-02 11:17:29 - Iter[151000], Epoch[000151], learning rate : 0.000198413, Train Loss: 0.142538771, Test MRAE: 0.192443103, Test RMSE: 0.027664255, Test PSNR: 32.931846619
2022-04-02 11:24:34 - Iter[152000], Epoch[000152], learning rate : 0.000196324, Train Loss: 0.142213345, Test MRAE: 0.202402875, Test RMSE: 0.031010529, Test PSNR: 32.218391418
2022-04-02 11:31:39 - Iter[153000], Epoch[000153], learning rate : 0.000194236, Train Loss: 0.141893104, Test MRAE: 0.178342223, Test RMSE: 0.025931854, Test PSNR: 33.731353760
2022-04-02 11:38:44 - Iter[154000], Epoch[000154], learning rate : 0.000192148, Train Loss: 0.141568884, Test MRAE: 0.195140392, Test RMSE: 0.027975077, Test PSNR: 33.036243439
2022-04-02 11:45:52 - Iter[155000], Epoch[000155], learning rate : 0.000190061, Train Loss: 0.141367212, Test MRAE: 0.194175407, Test RMSE: 0.029215315, Test PSNR: 32.724460602
2022-04-02 11:52:57 - Iter[156000], Epoch[000156], learning rate : 0.000187975, Train Loss: 0.141019657, Test MRAE: 0.193227798, Test RMSE: 0.028413054, Test PSNR: 32.936553955
2022-04-02 12:00:02 - Iter[157000], Epoch[000157], learning rate : 0.000185891, Train Loss: 0.140661910, Test MRAE: 0.184456378, Test RMSE: 0.026312418, Test PSNR: 33.953659058
2022-04-02 12:07:07 - Iter[158000], Epoch[000158], learning rate : 0.000183808, Train Loss: 0.140339255, Test MRAE: 0.185132653, Test RMSE: 0.028048612, Test PSNR: 33.299057007
2022-04-02 12:14:11 - Iter[159000], Epoch[000159], learning rate : 0.000181727, Train Loss: 0.140048787, Test MRAE: 0.200915650, Test RMSE: 0.029548207, Test PSNR: 32.610168457
2022-04-02 12:21:16 - Iter[160000], Epoch[000160], learning rate : 0.000179649, Train Loss: 0.139713675, Test MRAE: 0.185673729, Test RMSE: 0.026716650, Test PSNR: 33.573860168
2022-04-02 12:28:21 - Iter[161000], Epoch[000161], learning rate : 0.000177572, Train Loss: 0.139428943, Test MRAE: 0.199563235, Test RMSE: 0.028823234, Test PSNR: 32.666870117
2022-04-02 12:35:26 - Iter[162000], Epoch[000162], learning rate : 0.000175498, Train Loss: 0.139136240, Test MRAE: 0.186577111, Test RMSE: 0.027120251, Test PSNR: 33.629848480
2022-04-02 12:42:31 - Iter[163000], Epoch[000163], learning rate : 0.000173427, Train Loss: 0.138839096, Test MRAE: 0.208314449, Test RMSE: 0.029783567, Test PSNR: 33.040512085
2022-04-02 12:49:36 - Iter[164000], Epoch[000164], learning rate : 0.000171359, Train Loss: 0.138529077, Test MRAE: 0.183533952, Test RMSE: 0.026860280, Test PSNR: 33.956699371
2022-04-02 12:56:42 - Iter[165000], Epoch[000165], learning rate : 0.000169293, Train Loss: 0.138204068, Test MRAE: 0.184553340, Test RMSE: 0.027594643, Test PSNR: 33.348224640
2022-04-02 13:03:50 - Iter[166000], Epoch[000166], learning rate : 0.000167232, Train Loss: 0.137890905, Test MRAE: 0.184775501, Test RMSE: 0.027084967, Test PSNR: 33.471721649
2022-04-02 13:10:55 - Iter[167000], Epoch[000167], learning rate : 0.000165174, Train Loss: 0.137592033, Test MRAE: 0.190420717, Test RMSE: 0.027790477, Test PSNR: 33.165527344
2022-04-02 13:18:00 - Iter[168000], Epoch[000168], learning rate : 0.000163119, Train Loss: 0.137262672, Test MRAE: 0.189097628, Test RMSE: 0.028220892, Test PSNR: 33.251834869
2022-04-02 13:25:05 - Iter[169000], Epoch[000169], learning rate : 0.000161069, Train Loss: 0.136976957, Test MRAE: 0.200775757, Test RMSE: 0.029579053, Test PSNR: 32.661624908
2022-04-02 13:32:10 - Iter[170000], Epoch[000170], learning rate : 0.000159024, Train Loss: 0.136678070, Test MRAE: 0.203910515, Test RMSE: 0.029120363, Test PSNR: 32.741600037
2022-04-02 13:39:15 - Iter[171000], Epoch[000171], learning rate : 0.000156982, Train Loss: 0.136377513, Test MRAE: 0.185335383, Test RMSE: 0.027327787, Test PSNR: 33.279365540
2022-04-02 13:46:20 - Iter[172000], Epoch[000172], learning rate : 0.000154946, Train Loss: 0.136105239, Test MRAE: 0.188363656, Test RMSE: 0.026385780, Test PSNR: 33.601280212
2022-04-02 13:53:25 - Iter[173000], Epoch[000173], learning rate : 0.000152915, Train Loss: 0.135803401, Test MRAE: 0.188158661, Test RMSE: 0.027332157, Test PSNR: 33.062438965
2022-04-02 14:00:30 - Iter[174000], Epoch[000174], learning rate : 0.000150888, Train Loss: 0.135506123, Test MRAE: 0.179524571, Test RMSE: 0.025851950, Test PSNR: 33.738967896
2022-04-02 14:07:34 - Iter[175000], Epoch[000175], learning rate : 0.000148868, Train Loss: 0.135209709, Test MRAE: 0.201091379, Test RMSE: 0.027837001, Test PSNR: 32.859165192
2022-04-02 14:14:39 - Iter[176000], Epoch[000176], learning rate : 0.000146853, Train Loss: 0.134938180, Test MRAE: 0.180203870, Test RMSE: 0.027331300, Test PSNR: 33.304103851
2022-04-02 14:21:44 - Iter[177000], Epoch[000177], learning rate : 0.000144843, Train Loss: 0.134640649, Test MRAE: 0.184254661, Test RMSE: 0.026825031, Test PSNR: 33.661602020
2022-04-02 14:28:48 - Iter[178000], Epoch[000178], learning rate : 0.000142840, Train Loss: 0.134355381, Test MRAE: 0.188356847, Test RMSE: 0.027491307, Test PSNR: 33.094188690
2022-04-02 14:35:53 - Iter[179000], Epoch[000179], learning rate : 0.000140843, Train Loss: 0.134065256, Test MRAE: 0.182577327, Test RMSE: 0.026194653, Test PSNR: 33.313930511
2022-04-02 14:42:58 - Iter[180000], Epoch[000180], learning rate : 0.000138853, Train Loss: 0.133779585, Test MRAE: 0.180051744, Test RMSE: 0.026491985, Test PSNR: 33.679904938
2022-04-02 14:50:03 - Iter[181000], Epoch[000181], learning rate : 0.000136870, Train Loss: 0.133497566, Test MRAE: 0.191861689, Test RMSE: 0.028046003, Test PSNR: 32.966873169
2022-04-02 14:57:07 - Iter[182000], Epoch[000182], learning rate : 0.000134893, Train Loss: 0.133224696, Test MRAE: 0.176940173, Test RMSE: 0.025779530, Test PSNR: 33.985229492
2022-04-02 15:04:12 - Iter[183000], Epoch[000183], learning rate : 0.000132924, Train Loss: 0.132945195, Test MRAE: 0.182379216, Test RMSE: 0.026292851, Test PSNR: 33.578659058
2022-04-02 15:11:17 - Iter[184000], Epoch[000184], learning rate : 0.000130962, Train Loss: 0.132660449, Test MRAE: 0.181506500, Test RMSE: 0.026462642, Test PSNR: 33.512199402
2022-04-02 15:18:29 - Iter[185000], Epoch[000185], learning rate : 0.000129008, Train Loss: 0.132373899, Test MRAE: 0.185112610, Test RMSE: 0.026818167, Test PSNR: 33.537555695
2022-04-02 15:25:38 - Iter[186000], Epoch[000186], learning rate : 0.000127061, Train Loss: 0.132103384, Test MRAE: 0.174452588, Test RMSE: 0.025815820, Test PSNR: 33.743915558
2022-04-02 15:32:43 - Iter[187000], Epoch[000187], learning rate : 0.000125123, Train Loss: 0.131809682, Test MRAE: 0.187945589, Test RMSE: 0.027605584, Test PSNR: 33.280357361
2022-04-02 15:39:48 - Iter[188000], Epoch[000188], learning rate : 0.000123193, Train Loss: 0.131557167, Test MRAE: 0.188535050, Test RMSE: 0.027781457, Test PSNR: 33.184757233
2022-04-02 15:46:54 - Iter[189000], Epoch[000189], learning rate : 0.000121271, Train Loss: 0.131283060, Test MRAE: 0.185235590, Test RMSE: 0.027337948, Test PSNR: 33.403537750
2022-04-02 15:53:59 - Iter[190000], Epoch[000190], learning rate : 0.000119358, Train Loss: 0.131009027, Test MRAE: 0.181714118, Test RMSE: 0.027467228, Test PSNR: 33.468227386
2022-04-02 16:01:04 - Iter[191000], Epoch[000191], learning rate : 0.000117454, Train Loss: 0.130744711, Test MRAE: 0.182051897, Test RMSE: 0.025766656, Test PSNR: 33.840541840
2022-04-02 16:08:09 - Iter[192000], Epoch[000192], learning rate : 0.000115559, Train Loss: 0.130475432, Test MRAE: 0.172202274, Test RMSE: 0.025092792, Test PSNR: 34.193691254
2022-04-02 16:15:13 - Iter[193000], Epoch[000193], learning rate : 0.000113673, Train Loss: 0.130216569, Test MRAE: 0.184736788, Test RMSE: 0.026809329, Test PSNR: 33.624912262
2022-04-02 16:22:18 - Iter[194000], Epoch[000194], learning rate : 0.000111797, Train Loss: 0.129951045, Test MRAE: 0.182385370, Test RMSE: 0.026284508, Test PSNR: 33.886035919
2022-04-02 16:29:23 - Iter[195000], Epoch[000195], learning rate : 0.000109931, Train Loss: 0.129694492, Test MRAE: 0.175878316, Test RMSE: 0.025631424, Test PSNR: 34.065410614
2022-04-02 16:36:28 - Iter[196000], Epoch[000196], learning rate : 0.000108074, Train Loss: 0.129454225, Test MRAE: 0.179921359, Test RMSE: 0.026021415, Test PSNR: 33.702232361
2022-04-02 16:43:34 - Iter[197000], Epoch[000197], learning rate : 0.000106228, Train Loss: 0.129198417, Test MRAE: 0.175036028, Test RMSE: 0.024575345, Test PSNR: 34.425785065
2022-04-02 16:50:40 - Iter[198000], Epoch[000198], learning rate : 0.000104392, Train Loss: 0.102013312, Test MRAE: 0.177520946, Test RMSE: 0.025709214, Test PSNR: 33.887939453
2022-04-02 16:57:46 - Iter[199000], Epoch[000199], learning rate : 0.000102567, Train Loss: 0.103186131, Test MRAE: 0.180038393, Test RMSE: 0.026341597, Test PSNR: 33.402408600
2022-04-02 17:04:52 - Iter[200000], Epoch[000200], learning rate : 0.000100752, Train Loss: 0.103296362, Test MRAE: 0.173550472, Test RMSE: 0.025864061, Test PSNR: 34.057720184
2022-04-02 17:11:58 - Iter[201000], Epoch[000201], learning rate : 0.000098948, Train Loss: 0.103023626, Test MRAE: 0.167316645, Test RMSE: 0.025276201, Test PSNR: 34.202480316
2022-04-02 17:19:03 - Iter[202000], Epoch[000202], learning rate : 0.000097155, Train Loss: 0.102868967, Test MRAE: 0.188597649, Test RMSE: 0.027610108, Test PSNR: 33.283157349
2022-04-02 17:26:09 - Iter[203000], Epoch[000203], learning rate : 0.000095374, Train Loss: 0.102633081, Test MRAE: 0.184393466, Test RMSE: 0.026936097, Test PSNR: 33.632041931
2022-04-02 17:33:14 - Iter[204000], Epoch[000204], learning rate : 0.000093604, Train Loss: 0.102550589, Test MRAE: 0.170377418, Test RMSE: 0.025654864, Test PSNR: 34.075378418
2022-04-02 17:40:19 - Iter[205000], Epoch[000205], learning rate : 0.000091846, Train Loss: 0.102392547, Test MRAE: 0.178660944, Test RMSE: 0.025876619, Test PSNR: 33.723213196
2022-04-02 17:47:25 - Iter[206000], Epoch[000206], learning rate : 0.000090100, Train Loss: 0.102171890, Test MRAE: 0.176452860, Test RMSE: 0.024783300, Test PSNR: 34.127834320
2022-04-02 17:54:30 - Iter[207000], Epoch[000207], learning rate : 0.000088366, Train Loss: 0.102045104, Test MRAE: 0.186692208, Test RMSE: 0.026840849, Test PSNR: 33.744319916
2022-04-02 18:01:35 - Iter[208000], Epoch[000208], learning rate : 0.000086644, Train Loss: 0.101805404, Test MRAE: 0.177663192, Test RMSE: 0.026660608, Test PSNR: 33.983108521
2022-04-02 18:08:40 - Iter[209000], Epoch[000209], learning rate : 0.000084935, Train Loss: 0.101688534, Test MRAE: 0.180737436, Test RMSE: 0.026671521, Test PSNR: 33.593574524
2022-04-02 18:15:45 - Iter[210000], Epoch[000210], learning rate : 0.000083239, Train Loss: 0.101489715, Test MRAE: 0.177465603, Test RMSE: 0.026152683, Test PSNR: 33.800136566
2022-04-02 18:22:51 - Iter[211000], Epoch[000211], learning rate : 0.000081555, Train Loss: 0.101353914, Test MRAE: 0.175342098, Test RMSE: 0.025786392, Test PSNR: 33.923843384
2022-04-02 18:29:56 - Iter[212000], Epoch[000212], learning rate : 0.000079884, Train Loss: 0.101160653, Test MRAE: 0.173754156, Test RMSE: 0.025107183, Test PSNR: 34.212299347
2022-04-02 18:37:02 - Iter[213000], Epoch[000213], learning rate : 0.000078227, Train Loss: 0.101076037, Test MRAE: 0.183363929, Test RMSE: 0.025582314, Test PSNR: 33.679492950
2022-04-02 18:44:08 - Iter[214000], Epoch[000214], learning rate : 0.000076583, Train Loss: 0.100959152, Test MRAE: 0.183319777, Test RMSE: 0.026163427, Test PSNR: 33.530773163
2022-04-02 18:51:14 - Iter[215000], Epoch[000215], learning rate : 0.000074952, Train Loss: 0.100789256, Test MRAE: 0.178464502, Test RMSE: 0.025810177, Test PSNR: 33.969661713
2022-04-02 18:58:19 - Iter[216000], Epoch[000216], learning rate : 0.000073336, Train Loss: 0.100618713, Test MRAE: 0.181763679, Test RMSE: 0.026574261, Test PSNR: 33.871364594
2022-04-02 19:05:25 - Iter[217000], Epoch[000217], learning rate : 0.000071733, Train Loss: 0.100473806, Test MRAE: 0.187271968, Test RMSE: 0.028016690, Test PSNR: 33.208385468
2022-04-02 19:12:30 - Iter[218000], Epoch[000218], learning rate : 0.000070144, Train Loss: 0.100348599, Test MRAE: 0.179091349, Test RMSE: 0.026899850, Test PSNR: 33.474468231
2022-04-02 19:19:35 - Iter[219000], Epoch[000219], learning rate : 0.000068570, Train Loss: 0.100206099, Test MRAE: 0.184590265, Test RMSE: 0.026280979, Test PSNR: 33.707798004
2022-04-02 19:26:41 - Iter[220000], Epoch[000220], learning rate : 0.000067010, Train Loss: 0.100070745, Test MRAE: 0.175356671, Test RMSE: 0.026065310, Test PSNR: 33.811347961
2022-04-02 19:33:46 - Iter[221000], Epoch[000221], learning rate : 0.000065465, Train Loss: 0.099906124, Test MRAE: 0.169817805, Test RMSE: 0.024800271, Test PSNR: 34.327060699
2022-04-02 19:40:51 - Iter[222000], Epoch[000222], learning rate : 0.000063934, Train Loss: 0.099751174, Test MRAE: 0.182065576, Test RMSE: 0.026781045, Test PSNR: 33.564399719
2022-04-02 19:47:56 - Iter[223000], Epoch[000223], learning rate : 0.000062419, Train Loss: 0.099610791, Test MRAE: 0.170045108, Test RMSE: 0.024937458, Test PSNR: 34.150417328
2022-04-02 19:55:01 - Iter[224000], Epoch[000224], learning rate : 0.000060919, Train Loss: 0.099466614, Test MRAE: 0.164563686, Test RMSE: 0.024763588, Test PSNR: 34.316574097
2022-04-02 20:02:06 - Iter[225000], Epoch[000225], learning rate : 0.000059434, Train Loss: 0.099337809, Test MRAE: 0.169848636, Test RMSE: 0.025135307, Test PSNR: 34.368801117
2022-04-02 20:09:11 - Iter[226000], Epoch[000226], learning rate : 0.000057964, Train Loss: 0.099206202, Test MRAE: 0.175814182, Test RMSE: 0.025777623, Test PSNR: 33.952922821
2022-04-02 20:16:16 - Iter[227000], Epoch[000227], learning rate : 0.000056510, Train Loss: 0.099092752, Test MRAE: 0.180779472, Test RMSE: 0.026873387, Test PSNR: 33.692184448
2022-04-02 20:23:21 - Iter[228000], Epoch[000228], learning rate : 0.000055072, Train Loss: 0.098968647, Test MRAE: 0.176626131, Test RMSE: 0.026252782, Test PSNR: 33.874938965
2022-04-02 20:30:26 - Iter[229000], Epoch[000229], learning rate : 0.000053650, Train Loss: 0.098828159, Test MRAE: 0.176813632, Test RMSE: 0.025975049, Test PSNR: 33.953910828
2022-04-02 20:37:32 - Iter[230000], Epoch[000230], learning rate : 0.000052244, Train Loss: 0.098706461, Test MRAE: 0.183530748, Test RMSE: 0.027186699, Test PSNR: 33.595870972
2022-04-02 20:44:37 - Iter[231000], Epoch[000231], learning rate : 0.000050854, Train Loss: 0.098572075, Test MRAE: 0.172905609, Test RMSE: 0.025397453, Test PSNR: 34.208152771
2022-04-02 20:51:42 - Iter[232000], Epoch[000232], learning rate : 0.000049481, Train Loss: 0.098442763, Test MRAE: 0.171963841, Test RMSE: 0.024746301, Test PSNR: 34.430393219
2022-04-02 20:58:47 - Iter[233000], Epoch[000233], learning rate : 0.000048124, Train Loss: 0.098333366, Test MRAE: 0.169642508, Test RMSE: 0.024858534, Test PSNR: 34.272926331
2022-04-02 21:05:52 - Iter[234000], Epoch[000234], learning rate : 0.000046784, Train Loss: 0.098214746, Test MRAE: 0.175269395, Test RMSE: 0.025455236, Test PSNR: 34.277637482
2022-04-02 21:12:57 - Iter[235000], Epoch[000235], learning rate : 0.000045461, Train Loss: 0.098089337, Test MRAE: 0.179725915, Test RMSE: 0.026568489, Test PSNR: 33.739719391
2022-04-02 21:20:02 - Iter[236000], Epoch[000236], learning rate : 0.000044154, Train Loss: 0.097973123, Test MRAE: 0.172450393, Test RMSE: 0.025466623, Test PSNR: 34.136840820
2022-04-02 21:27:07 - Iter[237000], Epoch[000237], learning rate : 0.000042865, Train Loss: 0.097842306, Test MRAE: 0.176406667, Test RMSE: 0.025692917, Test PSNR: 33.947998047
2022-04-02 21:34:13 - Iter[238000], Epoch[000238], learning rate : 0.000041594, Train Loss: 0.097736515, Test MRAE: 0.171329692, Test RMSE: 0.024684755, Test PSNR: 34.304901123
2022-04-02 21:41:18 - Iter[239000], Epoch[000239], learning rate : 0.000040339, Train Loss: 0.097614735, Test MRAE: 0.173568949, Test RMSE: 0.025447363, Test PSNR: 34.016124725
2022-04-02 21:48:24 - Iter[240000], Epoch[000240], learning rate : 0.000039102, Train Loss: 0.097514018, Test MRAE: 0.173339590, Test RMSE: 0.025385490, Test PSNR: 34.295841217
2022-04-02 21:55:29 - Iter[241000], Epoch[000241], learning rate : 0.000037883, Train Loss: 0.097401790, Test MRAE: 0.169609487, Test RMSE: 0.024800491, Test PSNR: 34.456302643
2022-04-02 22:02:35 - Iter[242000], Epoch[000242], learning rate : 0.000036682, Train Loss: 0.097286768, Test MRAE: 0.170829326, Test RMSE: 0.024915423, Test PSNR: 34.406093597
2022-04-02 22:09:40 - Iter[243000], Epoch[000243], learning rate : 0.000035499, Train Loss: 0.097173542, Test MRAE: 0.169965848, Test RMSE: 0.024967100, Test PSNR: 34.404468536
2022-04-02 22:16:45 - Iter[244000], Epoch[000244], learning rate : 0.000034333, Train Loss: 0.097077407, Test MRAE: 0.171130821, Test RMSE: 0.025277352, Test PSNR: 34.101566315
2022-04-02 22:23:50 - Iter[245000], Epoch[000245], learning rate : 0.000033186, Train Loss: 0.096966311, Test MRAE: 0.169705361, Test RMSE: 0.024620878, Test PSNR: 34.398189545
2022-04-02 22:30:55 - Iter[246000], Epoch[000246], learning rate : 0.000032058, Train Loss: 0.096866965, Test MRAE: 0.171287298, Test RMSE: 0.025068011, Test PSNR: 34.359451294
2022-04-02 22:38:00 - Iter[247000], Epoch[000247], learning rate : 0.000030948, Train Loss: 0.096771702, Test MRAE: 0.171140552, Test RMSE: 0.024118789, Test PSNR: 34.412387848
2022-04-02 22:45:05 - Iter[248000], Epoch[000248], learning rate : 0.000029856, Train Loss: 0.096670911, Test MRAE: 0.173038691, Test RMSE: 0.024758296, Test PSNR: 34.242740631
2022-04-02 22:52:10 - Iter[249000], Epoch[000249], learning rate : 0.000028783, Train Loss: 0.096570656, Test MRAE: 0.175683975, Test RMSE: 0.025606265, Test PSNR: 33.980319977
2022-04-02 22:59:15 - Iter[250000], Epoch[000250], learning rate : 0.000027729, Train Loss: 0.096471980, Test MRAE: 0.172501862, Test RMSE: 0.025001653, Test PSNR: 34.208171844
2022-04-02 23:06:20 - Iter[251000], Epoch[000251], learning rate : 0.000026694, Train Loss: 0.096377127, Test MRAE: 0.175684333, Test RMSE: 0.025214545, Test PSNR: 34.126399994
2022-04-02 23:13:24 - Iter[252000], Epoch[000252], learning rate : 0.000025678, Train Loss: 0.096282974, Test MRAE: 0.168616459, Test RMSE: 0.025046522, Test PSNR: 34.348255157
2022-04-02 23:20:29 - Iter[253000], Epoch[000253], learning rate : 0.000024681, Train Loss: 0.096196659, Test MRAE: 0.174302474, Test RMSE: 0.025560383, Test PSNR: 34.046390533
2022-04-02 23:27:34 - Iter[254000], Epoch[000254], learning rate : 0.000023703, Train Loss: 0.096106492, Test MRAE: 0.172013313, Test RMSE: 0.025454707, Test PSNR: 34.170654297
2022-04-02 23:34:39 - Iter[255000], Epoch[000255], learning rate : 0.000022745, Train Loss: 0.096010938, Test MRAE: 0.176789179, Test RMSE: 0.025733352, Test PSNR: 34.026710510
2022-04-02 23:41:44 - Iter[256000], Epoch[000256], learning rate : 0.000021806, Train Loss: 0.095917232, Test MRAE: 0.170460507, Test RMSE: 0.024920849, Test PSNR: 34.431476593
2022-04-02 23:48:48 - Iter[257000], Epoch[000257], learning rate : 0.000020887, Train Loss: 0.095825307, Test MRAE: 0.177687988, Test RMSE: 0.025730435, Test PSNR: 34.037899017
2022-04-02 23:55:53 - Iter[258000], Epoch[000258], learning rate : 0.000019988, Train Loss: 0.095736593, Test MRAE: 0.177384824, Test RMSE: 0.025790937, Test PSNR: 33.950199127
2022-04-03 00:02:58 - Iter[259000], Epoch[000259], learning rate : 0.000019108, Train Loss: 0.095648848, Test MRAE: 0.172814712, Test RMSE: 0.025385153, Test PSNR: 34.214118958
2022-04-03 00:10:03 - Iter[260000], Epoch[000260], learning rate : 0.000018249, Train Loss: 0.095561735, Test MRAE: 0.176465437, Test RMSE: 0.025419867, Test PSNR: 34.152908325
2022-04-03 00:17:08 - Iter[261000], Epoch[000261], learning rate : 0.000017409, Train Loss: 0.095478170, Test MRAE: 0.173659295, Test RMSE: 0.025082523, Test PSNR: 34.286403656
2022-04-03 00:24:13 - Iter[262000], Epoch[000262], learning rate : 0.000016589, Train Loss: 0.095390625, Test MRAE: 0.174965188, Test RMSE: 0.025296332, Test PSNR: 34.228733063
2022-04-03 00:31:18 - Iter[263000], Epoch[000263], learning rate : 0.000015790, Train Loss: 0.095310710, Test MRAE: 0.173272014, Test RMSE: 0.025253400, Test PSNR: 34.215930939
2022-04-03 00:38:23 - Iter[264000], Epoch[000264], learning rate : 0.000015010, Train Loss: 0.095230654, Test MRAE: 0.172960177, Test RMSE: 0.025186719, Test PSNR: 34.282268524
2022-04-03 00:45:28 - Iter[265000], Epoch[000265], learning rate : 0.000014251, Train Loss: 0.095158212, Test MRAE: 0.172467500, Test RMSE: 0.024974264, Test PSNR: 34.298667908
2022-04-03 00:52:33 - Iter[266000], Epoch[000266], learning rate : 0.000013513, Train Loss: 0.095075481, Test MRAE: 0.173236698, Test RMSE: 0.025340516, Test PSNR: 34.142948151
2022-04-03 00:59:38 - Iter[267000], Epoch[000267], learning rate : 0.000012795, Train Loss: 0.094994411, Test MRAE: 0.173876226, Test RMSE: 0.025280485, Test PSNR: 34.186058044
2022-04-03 01:06:43 - Iter[268000], Epoch[000268], learning rate : 0.000012098, Train Loss: 0.094918594, Test MRAE: 0.172373906, Test RMSE: 0.025200335, Test PSNR: 34.231643677
2022-04-03 01:13:48 - Iter[269000], Epoch[000269], learning rate : 0.000011421, Train Loss: 0.094840132, Test MRAE: 0.176823452, Test RMSE: 0.025236448, Test PSNR: 34.183204651
2022-04-03 01:20:53 - Iter[270000], Epoch[000270], learning rate : 0.000010765, Train Loss: 0.094765984, Test MRAE: 0.174937800, Test RMSE: 0.025248336, Test PSNR: 34.247131348
2022-04-03 01:27:59 - Iter[271000], Epoch[000271], learning rate : 0.000010130, Train Loss: 0.094690360, Test MRAE: 0.175978959, Test RMSE: 0.025314970, Test PSNR: 34.188961029
2022-04-03 01:35:04 - Iter[272000], Epoch[000272], learning rate : 0.000009515, Train Loss: 0.094618134, Test MRAE: 0.173058748, Test RMSE: 0.025095370, Test PSNR: 34.204765320
2022-04-03 01:42:09 - Iter[273000], Epoch[000273], learning rate : 0.000008922, Train Loss: 0.094541267, Test MRAE: 0.175095826, Test RMSE: 0.025054602, Test PSNR: 34.264301300
2022-04-03 01:49:14 - Iter[274000], Epoch[000274], learning rate : 0.000008350, Train Loss: 0.094468683, Test MRAE: 0.174783930, Test RMSE: 0.025103582, Test PSNR: 34.187229156
2022-04-03 01:56:19 - Iter[275000], Epoch[000275], learning rate : 0.000007798, Train Loss: 0.094395339, Test MRAE: 0.174145490, Test RMSE: 0.025000006, Test PSNR: 34.295127869
2022-04-03 02:03:24 - Iter[276000], Epoch[000276], learning rate : 0.000007268, Train Loss: 0.094326064, Test MRAE: 0.172111481, Test RMSE: 0.024944454, Test PSNR: 34.385459900
2022-04-03 02:10:29 - Iter[277000], Epoch[000277], learning rate : 0.000006759, Train Loss: 0.094263680, Test MRAE: 0.172909528, Test RMSE: 0.024970975, Test PSNR: 34.289031982
2022-04-03 02:17:34 - Iter[278000], Epoch[000278], learning rate : 0.000006271, Train Loss: 0.094194099, Test MRAE: 0.173114032, Test RMSE: 0.025137685, Test PSNR: 34.249919891
2022-04-03 02:24:39 - Iter[279000], Epoch[000279], learning rate : 0.000005805, Train Loss: 0.094131619, Test MRAE: 0.172592431, Test RMSE: 0.025063267, Test PSNR: 34.253170013
2022-04-03 02:31:43 - Iter[280000], Epoch[000280], learning rate : 0.000005360, Train Loss: 0.094070241, Test MRAE: 0.172285095, Test RMSE: 0.024916217, Test PSNR: 34.321399689
2022-04-03 02:38:48 - Iter[281000], Epoch[000281], learning rate : 0.000004936, Train Loss: 0.094007894, Test MRAE: 0.173722848, Test RMSE: 0.025065776, Test PSNR: 34.265625000
2022-04-03 02:45:53 - Iter[282000], Epoch[000282], learning rate : 0.000004534, Train Loss: 0.093946725, Test MRAE: 0.173494712, Test RMSE: 0.024978532, Test PSNR: 34.287834167
2022-04-03 02:52:58 - Iter[283000], Epoch[000283], learning rate : 0.000004153, Train Loss: 0.093884036, Test MRAE: 0.174012259, Test RMSE: 0.025204217, Test PSNR: 34.203044891
2022-04-03 03:00:03 - Iter[284000], Epoch[000284], learning rate : 0.000003794, Train Loss: 0.093830213, Test MRAE: 0.174063355, Test RMSE: 0.025115388, Test PSNR: 34.260356903
2022-04-03 03:07:09 - Iter[285000], Epoch[000285], learning rate : 0.000003457, Train Loss: 0.093768492, Test MRAE: 0.173724785, Test RMSE: 0.025128247, Test PSNR: 34.205513000
2022-04-03 03:14:14 - Iter[286000], Epoch[000286], learning rate : 0.000003140, Train Loss: 0.093710221, Test MRAE: 0.174080461, Test RMSE: 0.025255309, Test PSNR: 34.210517883
2022-04-03 03:21:20 - Iter[287000], Epoch[000287], learning rate : 0.000002846, Train Loss: 0.093652502, Test MRAE: 0.173344612, Test RMSE: 0.025169428, Test PSNR: 34.228454590
2022-04-03 03:28:25 - Iter[288000], Epoch[000288], learning rate : 0.000002573, Train Loss: 0.093591094, Test MRAE: 0.174104929, Test RMSE: 0.025068779, Test PSNR: 34.279476166
2022-04-03 03:35:30 - Iter[289000], Epoch[000289], learning rate : 0.000002322, Train Loss: 0.093536645, Test MRAE: 0.174188331, Test RMSE: 0.025058277, Test PSNR: 34.253150940
2022-04-03 03:42:35 - Iter[290000], Epoch[000290], learning rate : 0.000002093, Train Loss: 0.093481854, Test MRAE: 0.174243420, Test RMSE: 0.025219321, Test PSNR: 34.190120697
2022-04-03 03:49:40 - Iter[291000], Epoch[000291], learning rate : 0.000001886, Train Loss: 0.093428098, Test MRAE: 0.174073458, Test RMSE: 0.025161711, Test PSNR: 34.223892212
2022-04-03 03:56:45 - Iter[292000], Epoch[000292], learning rate : 0.000001700, Train Loss: 0.093377687, Test MRAE: 0.174377516, Test RMSE: 0.025047576, Test PSNR: 34.251167297
2022-04-03 04:03:50 - Iter[293000], Epoch[000293], learning rate : 0.000001536, Train Loss: 0.093325295, Test MRAE: 0.174216688, Test RMSE: 0.025174670, Test PSNR: 34.216476440
2022-04-03 04:10:55 - Iter[294000], Epoch[000294], learning rate : 0.000001394, Train Loss: 0.093274005, Test MRAE: 0.174724340, Test RMSE: 0.025191264, Test PSNR: 34.199459076
2022-04-03 04:18:00 - Iter[295000], Epoch[000295], learning rate : 0.000001274, Train Loss: 0.093229152, Test MRAE: 0.174318507, Test RMSE: 0.025124440, Test PSNR: 34.229160309
2022-04-03 04:25:06 - Iter[296000], Epoch[000296], learning rate : 0.000001175, Train Loss: 0.093180746, Test MRAE: 0.174350277, Test RMSE: 0.025153849, Test PSNR: 34.227703094
2022-04-03 04:32:11 - Iter[297000], Epoch[000297], learning rate : 0.000001099, Train Loss: 0.088473551, Test MRAE: 0.174573511, Test RMSE: 0.025096692, Test PSNR: 34.242698669
2022-04-03 04:39:17 - Iter[298000], Epoch[000298], learning rate : 0.000001044, Train Loss: 0.088746868, Test MRAE: 0.175092638, Test RMSE: 0.025211535, Test PSNR: 34.226341248
2022-04-03 04:46:22 - Iter[299000], Epoch[000299], learning rate : 0.000001011, Train Loss: 0.088239878, Test MRAE: 0.175596133, Test RMSE: 0.025226230, Test PSNR: 34.199283600
2022-04-03 04:53:28 - Iter[300000], Epoch[000300], learning rate : 0.000001000, Train Loss: 0.088261627, Test MRAE: 0.174593732, Test RMSE: 0.025195710, Test PSNR: 34.227554321
2022-04-03 05:00:35 - Iter[301000], Epoch[000301], learning rate : 0.000001011, Train Loss: 0.088277444, Test MRAE: 0.174352035, Test RMSE: 0.025199976, Test PSNR: 34.226184845
2022-04-03 05:07:40 - Iter[302000], Epoch[000302], learning rate : 0.000001044, Train Loss: 0.088294074, Test MRAE: 0.174182013, Test RMSE: 0.025130723, Test PSNR: 34.256118774
2022-04-03 05:14:44 - Iter[303000], Epoch[000303], learning rate : 0.000001098, Train Loss: 0.088272475, Test MRAE: 0.174258068, Test RMSE: 0.025136014, Test PSNR: 34.253238678
2022-04-03 05:21:49 - Iter[304000], Epoch[000304], learning rate : 0.000001175, Train Loss: 0.088300489, Test MRAE: 0.173932657, Test RMSE: 0.025094602, Test PSNR: 34.246120453
2022-04-03 05:28:54 - Iter[305000], Epoch[000305], learning rate : 0.000001273, Train Loss: 0.088291660, Test MRAE: 0.174736366, Test RMSE: 0.025204089, Test PSNR: 34.193691254
2022-04-03 05:35:59 - Iter[306000], Epoch[000306], learning rate : 0.000001394, Train Loss: 0.088330865, Test MRAE: 0.174246892, Test RMSE: 0.025176624, Test PSNR: 34.195224762
2022-04-03 05:43:03 - Iter[307000], Epoch[000307], learning rate : 0.000001536, Train Loss: 0.088328935, Test MRAE: 0.175528884, Test RMSE: 0.025345746, Test PSNR: 34.156028748
2022-04-03 05:50:08 - Iter[308000], Epoch[000308], learning rate : 0.000001699, Train Loss: 0.088340200, Test MRAE: 0.174348667, Test RMSE: 0.025237778, Test PSNR: 34.197441101
2022-04-03 05:57:13 - Iter[309000], Epoch[000309], learning rate : 0.000001885, Train Loss: 0.088360876, Test MRAE: 0.174186900, Test RMSE: 0.025157742, Test PSNR: 34.239955902
2022-04-03 06:04:18 - Iter[310000], Epoch[000310], learning rate : 0.000002093, Train Loss: 0.088359281, Test MRAE: 0.174402595, Test RMSE: 0.025201622, Test PSNR: 34.236080170
2022-04-03 06:11:23 - Iter[311000], Epoch[000311], learning rate : 0.000002322, Train Loss: 0.088350669, Test MRAE: 0.173989639, Test RMSE: 0.025123596, Test PSNR: 34.244651794
2022-04-03 06:18:28 - Iter[312000], Epoch[000312], learning rate : 0.000002573, Train Loss: 0.088332266, Test MRAE: 0.174378604, Test RMSE: 0.025167817, Test PSNR: 34.245853424
2022-04-03 06:25:33 - Iter[313000], Epoch[000313], learning rate : 0.000002846, Train Loss: 0.088298693, Test MRAE: 0.174335554, Test RMSE: 0.025195438, Test PSNR: 34.211570740
2022-04-03 06:32:38 - Iter[314000], Epoch[000314], learning rate : 0.000003140, Train Loss: 0.088309810, Test MRAE: 0.173345357, Test RMSE: 0.025142780, Test PSNR: 34.275386810
2022-04-03 06:39:43 - Iter[315000], Epoch[000315], learning rate : 0.000003456, Train Loss: 0.088285938, Test MRAE: 0.173520312, Test RMSE: 0.025105102, Test PSNR: 34.258850098
2022-04-03 06:46:48 - Iter[316000], Epoch[000316], learning rate : 0.000003793, Train Loss: 0.088285491, Test MRAE: 0.173349351, Test RMSE: 0.025218228, Test PSNR: 34.208095551
2022-04-03 06:53:53 - Iter[317000], Epoch[000317], learning rate : 0.000004153, Train Loss: 0.088280156, Test MRAE: 0.173202068, Test RMSE: 0.024939898, Test PSNR: 34.307556152
2022-04-03 07:00:58 - Iter[318000], Epoch[000318], learning rate : 0.000004533, Train Loss: 0.088280633, Test MRAE: 0.173844755, Test RMSE: 0.025200803, Test PSNR: 34.239402771
2022-04-03 07:08:03 - Iter[319000], Epoch[000319], learning rate : 0.000004935, Train Loss: 0.088273644, Test MRAE: 0.172965735, Test RMSE: 0.025151666, Test PSNR: 34.252388000
2022-04-03 07:15:08 - Iter[320000], Epoch[000320], learning rate : 0.000005359, Train Loss: 0.088286422, Test MRAE: 0.174557626, Test RMSE: 0.025143858, Test PSNR: 34.216236115
2022-04-03 07:22:13 - Iter[321000], Epoch[000321], learning rate : 0.000005804, Train Loss: 0.088279031, Test MRAE: 0.174165010, Test RMSE: 0.025121007, Test PSNR: 34.277862549
2022-04-03 07:29:18 - Iter[322000], Epoch[000322], learning rate : 0.000006271, Train Loss: 0.088302597, Test MRAE: 0.173597291, Test RMSE: 0.025011525, Test PSNR: 34.285095215
2022-04-03 07:36:23 - Iter[323000], Epoch[000323], learning rate : 0.000006758, Train Loss: 0.088292696, Test MRAE: 0.175200403, Test RMSE: 0.025254402, Test PSNR: 34.246257782
2022-04-03 07:43:28 - Iter[324000], Epoch[000324], learning rate : 0.000007267, Train Loss: 0.088295013, Test MRAE: 0.173671618, Test RMSE: 0.025014060, Test PSNR: 34.307495117
2022-04-03 07:50:34 - Iter[325000], Epoch[000325], learning rate : 0.000007797, Train Loss: 0.088305362, Test MRAE: 0.173001438, Test RMSE: 0.024917930, Test PSNR: 34.304939270
2022-04-03 07:57:39 - Iter[326000], Epoch[000326], learning rate : 0.000008349, Train Loss: 0.088295788, Test MRAE: 0.175064057, Test RMSE: 0.025243279, Test PSNR: 34.163051605
2022-04-03 08:04:44 - Iter[327000], Epoch[000327], learning rate : 0.000008921, Train Loss: 0.088311076, Test MRAE: 0.173743173, Test RMSE: 0.025182966, Test PSNR: 34.231189728
2022-04-03 08:11:49 - Iter[328000], Epoch[000328], learning rate : 0.000009514, Train Loss: 0.088314973, Test MRAE: 0.173278883, Test RMSE: 0.025028666, Test PSNR: 34.261184692
2022-04-03 08:18:55 - Iter[329000], Epoch[000329], learning rate : 0.000010128, Train Loss: 0.088327415, Test MRAE: 0.172499105, Test RMSE: 0.024924604, Test PSNR: 34.274436951
2022-04-03 08:26:00 - Iter[330000], Epoch[000330], learning rate : 0.000010764, Train Loss: 0.088329569, Test MRAE: 0.175183654, Test RMSE: 0.025248297, Test PSNR: 34.206916809
2022-04-03 08:33:05 - Iter[331000], Epoch[000331], learning rate : 0.000011420, Train Loss: 0.088340379, Test MRAE: 0.175171733, Test RMSE: 0.025105324, Test PSNR: 34.237407684
2022-04-03 08:40:10 - Iter[332000], Epoch[000332], learning rate : 0.000012096, Train Loss: 0.088350333, Test MRAE: 0.177048355, Test RMSE: 0.025380399, Test PSNR: 34.125144958
2022-04-03 08:47:15 - Iter[333000], Epoch[000333], learning rate : 0.000012794, Train Loss: 0.088376462, Test MRAE: 0.175898567, Test RMSE: 0.025281807, Test PSNR: 34.227268219
2022-04-03 08:54:21 - Iter[334000], Epoch[000334], learning rate : 0.000013512, Train Loss: 0.088379711, Test MRAE: 0.176106483, Test RMSE: 0.025645779, Test PSNR: 34.148567200
2022-04-03 09:01:26 - Iter[335000], Epoch[000335], learning rate : 0.000014250, Train Loss: 0.088380866, Test MRAE: 0.174269333, Test RMSE: 0.025240457, Test PSNR: 34.278079987
2022-04-03 09:08:31 - Iter[336000], Epoch[000336], learning rate : 0.000015009, Train Loss: 0.088379689, Test MRAE: 0.176254421, Test RMSE: 0.025537958, Test PSNR: 34.039653778
2022-04-03 09:15:36 - Iter[337000], Epoch[000337], learning rate : 0.000015788, Train Loss: 0.088390432, Test MRAE: 0.175960690, Test RMSE: 0.025410790, Test PSNR: 34.169429779
2022-04-03 09:22:41 - Iter[338000], Epoch[000338], learning rate : 0.000016587, Train Loss: 0.088406108, Test MRAE: 0.179115504, Test RMSE: 0.025694687, Test PSNR: 34.006301880
2022-04-03 09:29:46 - Iter[339000], Epoch[000339], learning rate : 0.000017407, Train Loss: 0.088411152, Test MRAE: 0.173014969, Test RMSE: 0.025215123, Test PSNR: 34.278945923
2022-04-23 14:54:07 - Iter[001000], Epoch[000001], learning rate : 0.000399989, Train Loss: 0.580967367, Test MRAE: 0.476128250, Test RMSE: 0.071309306, Test PSNR: 25.389589310
2022-04-23 15:01:27 - Iter[002000], Epoch[000002], learning rate : 0.000399956, Train Loss: 0.537868500, Test MRAE: 0.383601397, Test RMSE: 0.059793666, Test PSNR: 26.869729996
2022-04-23 15:08:50 - Iter[003000], Epoch[000003], learning rate : 0.000399902, Train Loss: 0.514853716, Test MRAE: 0.394130856, Test RMSE: 0.065862104, Test PSNR: 26.205434799
2022-04-23 15:16:10 - Iter[004000], Epoch[000004], learning rate : 0.000399825, Train Loss: 0.500651658, Test MRAE: 0.345739305, Test RMSE: 0.054274678, Test PSNR: 27.642354965
2022-04-23 15:23:28 - Iter[005000], Epoch[000005], learning rate : 0.000399727, Train Loss: 0.488659710, Test MRAE: 0.363927722, Test RMSE: 0.061550226, Test PSNR: 27.183994293
2022-04-23 15:30:47 - Iter[006000], Epoch[000006], learning rate : 0.000399606, Train Loss: 0.479056925, Test MRAE: 0.350590169, Test RMSE: 0.057620656, Test PSNR: 27.573356628
2022-04-23 15:38:06 - Iter[007000], Epoch[000007], learning rate : 0.000399464, Train Loss: 0.471779317, Test MRAE: 0.366796970, Test RMSE: 0.065698914, Test PSNR: 26.534322739
2022-04-23 15:45:25 - Iter[008000], Epoch[000008], learning rate : 0.000399301, Train Loss: 0.465759695, Test MRAE: 0.311092198, Test RMSE: 0.051339153, Test PSNR: 28.427770615
2022-04-23 15:52:44 - Iter[009000], Epoch[000009], learning rate : 0.000399115, Train Loss: 0.460264921, Test MRAE: 0.313555092, Test RMSE: 0.048982244, Test PSNR: 28.425634384
2022-04-23 16:00:04 - Iter[010000], Epoch[000010], learning rate : 0.000398907, Train Loss: 0.455360144, Test MRAE: 0.382873207, Test RMSE: 0.060874436, Test PSNR: 27.476076126
2022-04-23 16:07:23 - Iter[011000], Epoch[000011], learning rate : 0.000398678, Train Loss: 0.450110734, Test MRAE: 0.401376307, Test RMSE: 0.060107835, Test PSNR: 26.758531570
2022-04-23 16:14:42 - Iter[012000], Epoch[000012], learning rate : 0.000398427, Train Loss: 0.443101197, Test MRAE: 0.367389202, Test RMSE: 0.064002097, Test PSNR: 26.256797791
2022-04-23 16:22:01 - Iter[013000], Epoch[000013], learning rate : 0.000398154, Train Loss: 0.435287356, Test MRAE: 0.330284655, Test RMSE: 0.050466936, Test PSNR: 28.128953934
2022-04-23 16:29:20 - Iter[014000], Epoch[000014], learning rate : 0.000397860, Train Loss: 0.427304626, Test MRAE: 0.296338409, Test RMSE: 0.044718273, Test PSNR: 29.178955078
2022-04-23 16:36:39 - Iter[015000], Epoch[000015], learning rate : 0.000397544, Train Loss: 0.419607937, Test MRAE: 0.377029449, Test RMSE: 0.059801549, Test PSNR: 26.841163635
2022-04-23 16:43:59 - Iter[016000], Epoch[000016], learning rate : 0.000397207, Train Loss: 0.412444025, Test MRAE: 0.241100386, Test RMSE: 0.038382120, Test PSNR: 30.745235443
2022-04-23 16:51:21 - Iter[017000], Epoch[000017], learning rate : 0.000396847, Train Loss: 0.405769527, Test MRAE: 0.293892324, Test RMSE: 0.046479706, Test PSNR: 29.053001404
2022-04-23 16:58:41 - Iter[018000], Epoch[000018], learning rate : 0.000396467, Train Loss: 0.399653912, Test MRAE: 0.265503436, Test RMSE: 0.039383758, Test PSNR: 30.118173599
2022-04-23 17:06:02 - Iter[019000], Epoch[000019], learning rate : 0.000396065, Train Loss: 0.393961877, Test MRAE: 0.218993932, Test RMSE: 0.033708785, Test PSNR: 31.340415955
2022-04-23 17:13:22 - Iter[020000], Epoch[000020], learning rate : 0.000395641, Train Loss: 0.388909101, Test MRAE: 0.318373829, Test RMSE: 0.047881890, Test PSNR: 28.579481125
2022-04-23 17:20:42 - Iter[021000], Epoch[000021], learning rate : 0.000395196, Train Loss: 0.383834749, Test MRAE: 0.260566443, Test RMSE: 0.040972795, Test PSNR: 30.061632156
2022-04-23 17:28:01 - Iter[022000], Epoch[000022], learning rate : 0.000394729, Train Loss: 0.379185855, Test MRAE: 0.235527620, Test RMSE: 0.031463142, Test PSNR: 32.066135406
2022-04-23 17:35:19 - Iter[023000], Epoch[000023], learning rate : 0.000394242, Train Loss: 0.374649942, Test MRAE: 0.316346705, Test RMSE: 0.047554474, Test PSNR: 28.483852386
2022-04-23 17:42:37 - Iter[024000], Epoch[000024], learning rate : 0.000393733, Train Loss: 0.370376557, Test MRAE: 0.253135085, Test RMSE: 0.039008230, Test PSNR: 30.486635208
2022-04-23 17:49:56 - Iter[025000], Epoch[000025], learning rate : 0.000393203, Train Loss: 0.366444081, Test MRAE: 0.237607956, Test RMSE: 0.036305230, Test PSNR: 30.948564529
2022-04-23 17:57:14 - Iter[026000], Epoch[000026], learning rate : 0.000392651, Train Loss: 0.362563521, Test MRAE: 0.222553328, Test RMSE: 0.034099448, Test PSNR: 31.294393539
2022-04-23 18:04:32 - Iter[027000], Epoch[000027], learning rate : 0.000392079, Train Loss: 0.358996868, Test MRAE: 0.239717230, Test RMSE: 0.035883091, Test PSNR: 30.893278122
2022-04-23 18:11:51 - Iter[028000], Epoch[000028], learning rate : 0.000391486, Train Loss: 0.355459571, Test MRAE: 0.257825851, Test RMSE: 0.038511395, Test PSNR: 30.400640488
2022-04-23 18:19:08 - Iter[029000], Epoch[000029], learning rate : 0.000390872, Train Loss: 0.352386326, Test MRAE: 0.263031691, Test RMSE: 0.040168904, Test PSNR: 30.280921936
2022-04-23 18:26:27 - Iter[030000], Epoch[000030], learning rate : 0.000390236, Train Loss: 0.349313766, Test MRAE: 0.251482666, Test RMSE: 0.037624672, Test PSNR: 30.593528748
2022-04-23 18:33:45 - Iter[031000], Epoch[000031], learning rate : 0.000389580, Train Loss: 0.346212476, Test MRAE: 0.298717499, Test RMSE: 0.045085624, Test PSNR: 28.977834702
2022-04-23 18:41:03 - Iter[032000], Epoch[000032], learning rate : 0.000388904, Train Loss: 0.343339562, Test MRAE: 0.208545208, Test RMSE: 0.030257983, Test PSNR: 32.388492584
2022-04-23 18:48:21 - Iter[033000], Epoch[000033], learning rate : 0.000388206, Train Loss: 0.340497434, Test MRAE: 0.218899906, Test RMSE: 0.034442928, Test PSNR: 31.635265350
2022-04-23 18:55:39 - Iter[034000], Epoch[000034], learning rate : 0.000387488, Train Loss: 0.337937385, Test MRAE: 0.241465181, Test RMSE: 0.037448876, Test PSNR: 30.588479996
2022-04-23 19:02:58 - Iter[035000], Epoch[000035], learning rate : 0.000386750, Train Loss: 0.335323632, Test MRAE: 0.275378555, Test RMSE: 0.039911207, Test PSNR: 30.132581711
2022-04-23 19:10:16 - Iter[036000], Epoch[000036], learning rate : 0.000385991, Train Loss: 0.332810342, Test MRAE: 0.249986067, Test RMSE: 0.037473664, Test PSNR: 30.648197174
2022-04-23 19:17:34 - Iter[037000], Epoch[000037], learning rate : 0.000385212, Train Loss: 0.330464005, Test MRAE: 0.203446269, Test RMSE: 0.032859102, Test PSNR: 31.923265457
2022-04-23 19:24:53 - Iter[038000], Epoch[000038], learning rate : 0.000384413, Train Loss: 0.328254640, Test MRAE: 0.219546139, Test RMSE: 0.030268494, Test PSNR: 32.461910248
2022-04-23 19:32:11 - Iter[039000], Epoch[000039], learning rate : 0.000383593, Train Loss: 0.325922102, Test MRAE: 0.237117186, Test RMSE: 0.035817865, Test PSNR: 30.921569824
2022-04-23 19:39:29 - Iter[040000], Epoch[000040], learning rate : 0.000382753, Train Loss: 0.323781013, Test MRAE: 0.230836883, Test RMSE: 0.034642611, Test PSNR: 31.312885284
2022-04-23 19:46:47 - Iter[041000], Epoch[000041], learning rate : 0.000381893, Train Loss: 0.321719289, Test MRAE: 0.269062251, Test RMSE: 0.038956814, Test PSNR: 30.153285980
2022-04-23 19:54:06 - Iter[042000], Epoch[000042], learning rate : 0.000381014, Train Loss: 0.319605142, Test MRAE: 0.202474520, Test RMSE: 0.031404991, Test PSNR: 32.427246094
2022-04-23 20:01:25 - Iter[043000], Epoch[000043], learning rate : 0.000380115, Train Loss: 0.317672819, Test MRAE: 0.236959890, Test RMSE: 0.034768596, Test PSNR: 31.079244614
2022-04-23 20:08:43 - Iter[044000], Epoch[000044], learning rate : 0.000379195, Train Loss: 0.315703183, Test MRAE: 0.238693506, Test RMSE: 0.038637117, Test PSNR: 30.511718750
2022-04-23 20:16:05 - Iter[045000], Epoch[000045], learning rate : 0.000378257, Train Loss: 0.313840389, Test MRAE: 0.200682059, Test RMSE: 0.032137647, Test PSNR: 31.960329056
2022-04-23 20:23:25 - Iter[046000], Epoch[000046], learning rate : 0.000377299, Train Loss: 0.311921746, Test MRAE: 0.192481488, Test RMSE: 0.029860133, Test PSNR: 32.977344513
2022-04-23 20:30:45 - Iter[047000], Epoch[000047], learning rate : 0.000376321, Train Loss: 0.310140073, Test MRAE: 0.216454044, Test RMSE: 0.030876338, Test PSNR: 32.372810364
2022-04-23 20:38:05 - Iter[048000], Epoch[000048], learning rate : 0.000375324, Train Loss: 0.308418721, Test MRAE: 0.222013146, Test RMSE: 0.033111215, Test PSNR: 31.757154465
2022-04-23 20:45:25 - Iter[049000], Epoch[000049], learning rate : 0.000374308, Train Loss: 0.306717455, Test MRAE: 0.237809107, Test RMSE: 0.034070566, Test PSNR: 31.445192337
2022-04-23 20:52:46 - Iter[050000], Epoch[000050], learning rate : 0.000373273, Train Loss: 0.305099010, Test MRAE: 0.202651635, Test RMSE: 0.030802425, Test PSNR: 32.522060394
2022-04-23 21:00:05 - Iter[051000], Epoch[000051], learning rate : 0.000372219, Train Loss: 0.303440392, Test MRAE: 0.217300177, Test RMSE: 0.032439526, Test PSNR: 31.829879761
2022-04-23 21:07:32 - Iter[052000], Epoch[000052], learning rate : 0.000371146, Train Loss: 0.301833332, Test MRAE: 0.204130247, Test RMSE: 0.029276503, Test PSNR: 32.545211792
2022-04-23 21:15:00 - Iter[053000], Epoch[000053], learning rate : 0.000370055, Train Loss: 0.300291687, Test MRAE: 0.220381573, Test RMSE: 0.032079305, Test PSNR: 32.000339508
2022-04-23 21:22:25 - Iter[054000], Epoch[000054], learning rate : 0.000368945, Train Loss: 0.298797071, Test MRAE: 0.223565817, Test RMSE: 0.034516092, Test PSNR: 31.357244492
2022-04-23 21:29:53 - Iter[055000], Epoch[000055], learning rate : 0.000367816, Train Loss: 0.297255069, Test MRAE: 0.217861712, Test RMSE: 0.033433646, Test PSNR: 31.472415924
2022-04-23 21:37:21 - Iter[056000], Epoch[000056], learning rate : 0.000366669, Train Loss: 0.295802027, Test MRAE: 0.217024893, Test RMSE: 0.031114863, Test PSNR: 32.060291290
2022-04-23 21:44:46 - Iter[057000], Epoch[000057], learning rate : 0.000365504, Train Loss: 0.294379652, Test MRAE: 0.200093538, Test RMSE: 0.029881733, Test PSNR: 32.633159637
2022-04-23 21:52:14 - Iter[058000], Epoch[000058], learning rate : 0.000364320, Train Loss: 0.292981386, Test MRAE: 0.194833234, Test RMSE: 0.029647592, Test PSNR: 32.831436157
2022-04-23 21:59:39 - Iter[059000], Epoch[000059], learning rate : 0.000363119, Train Loss: 0.291627407, Test MRAE: 0.197801381, Test RMSE: 0.030740781, Test PSNR: 32.337741852
2022-04-23 22:07:04 - Iter[060000], Epoch[000060], learning rate : 0.000361900, Train Loss: 0.290296584, Test MRAE: 0.197543964, Test RMSE: 0.030674493, Test PSNR: 32.592945099
2022-04-23 22:14:30 - Iter[061000], Epoch[000061], learning rate : 0.000360663, Train Loss: 0.289049506, Test MRAE: 0.244868755, Test RMSE: 0.036923688, Test PSNR: 30.769796371
2022-04-23 22:21:55 - Iter[062000], Epoch[000062], learning rate : 0.000359409, Train Loss: 0.287759632, Test MRAE: 0.195578441, Test RMSE: 0.030247275, Test PSNR: 32.560684204
2022-04-23 22:29:19 - Iter[063000], Epoch[000063], learning rate : 0.000358137, Train Loss: 0.286486626, Test MRAE: 0.193518609, Test RMSE: 0.029375948, Test PSNR: 32.727729797
2022-04-23 22:36:47 - Iter[064000], Epoch[000064], learning rate : 0.000356848, Train Loss: 0.285266459, Test MRAE: 0.207192123, Test RMSE: 0.032270975, Test PSNR: 32.110836029
2022-04-23 22:44:11 - Iter[065000], Epoch[000065], learning rate : 0.000355542, Train Loss: 0.284025401, Test MRAE: 0.207033843, Test RMSE: 0.031536400, Test PSNR: 31.952850342
2022-04-23 22:51:36 - Iter[066000], Epoch[000066], learning rate : 0.000354219, Train Loss: 0.282821357, Test MRAE: 0.204611927, Test RMSE: 0.031062832, Test PSNR: 32.402847290
2022-04-23 22:59:05 - Iter[067000], Epoch[000067], learning rate : 0.000352879, Train Loss: 0.281657964, Test MRAE: 0.211089820, Test RMSE: 0.032642465, Test PSNR: 31.807523727
2022-04-23 23:06:30 - Iter[068000], Epoch[000068], learning rate : 0.000351522, Train Loss: 0.280515015, Test MRAE: 0.207025707, Test RMSE: 0.032119859, Test PSNR: 32.091171265
2022-04-23 23:13:50 - Iter[069000], Epoch[000069], learning rate : 0.000350149, Train Loss: 0.279397517, Test MRAE: 0.201888502, Test RMSE: 0.029915432, Test PSNR: 32.756103516
2022-04-23 23:21:11 - Iter[070000], Epoch[000070], learning rate : 0.000348759, Train Loss: 0.278299868, Test MRAE: 0.199837267, Test RMSE: 0.030389279, Test PSNR: 32.478157043
2022-04-23 23:28:31 - Iter[071000], Epoch[000071], learning rate : 0.000347353, Train Loss: 0.277205318, Test MRAE: 0.192629695, Test RMSE: 0.028528344, Test PSNR: 32.856678009
2022-04-23 23:35:52 - Iter[072000], Epoch[000072], learning rate : 0.000345931, Train Loss: 0.276128292, Test MRAE: 0.208476946, Test RMSE: 0.031134097, Test PSNR: 32.529979706
2022-04-23 23:43:13 - Iter[073000], Epoch[000073], learning rate : 0.000344493, Train Loss: 0.275062859, Test MRAE: 0.182777673, Test RMSE: 0.027929183, Test PSNR: 33.378974915
2022-04-23 23:50:33 - Iter[074000], Epoch[000074], learning rate : 0.000343039, Train Loss: 0.273981810, Test MRAE: 0.195108235, Test RMSE: 0.029721884, Test PSNR: 32.791049957
2022-04-23 23:57:54 - Iter[075000], Epoch[000075], learning rate : 0.000341569, Train Loss: 0.272962540, Test MRAE: 0.190681145, Test RMSE: 0.029655520, Test PSNR: 32.704948425
2022-04-24 00:05:15 - Iter[076000], Epoch[000076], learning rate : 0.000340084, Train Loss: 0.271951467, Test MRAE: 0.184739619, Test RMSE: 0.028160227, Test PSNR: 33.141639709
2022-04-24 00:12:35 - Iter[077000], Epoch[000077], learning rate : 0.000338584, Train Loss: 0.270981908, Test MRAE: 0.228441313, Test RMSE: 0.035949882, Test PSNR: 31.061113358
2022-04-24 00:19:55 - Iter[078000], Epoch[000078], learning rate : 0.000337069, Train Loss: 0.269994497, Test MRAE: 0.197942689, Test RMSE: 0.029749321, Test PSNR: 32.555427551
2022-04-24 00:27:16 - Iter[079000], Epoch[000079], learning rate : 0.000335538, Train Loss: 0.269016474, Test MRAE: 0.193317026, Test RMSE: 0.028383691, Test PSNR: 33.051807404
2022-04-24 00:34:35 - Iter[080000], Epoch[000080], learning rate : 0.000333993, Train Loss: 0.268079102, Test MRAE: 0.195552021, Test RMSE: 0.029874822, Test PSNR: 32.708820343
2022-04-24 00:41:55 - Iter[081000], Epoch[000081], learning rate : 0.000332433, Train Loss: 0.267140716, Test MRAE: 0.226891816, Test RMSE: 0.033469871, Test PSNR: 31.563917160
2022-04-24 00:49:14 - Iter[082000], Epoch[000082], learning rate : 0.000330859, Train Loss: 0.266172856, Test MRAE: 0.201588601, Test RMSE: 0.031107217, Test PSNR: 32.103111267
2022-04-24 00:56:34 - Iter[083000], Epoch[000083], learning rate : 0.000329270, Train Loss: 0.265231282, Test MRAE: 0.210780159, Test RMSE: 0.031387288, Test PSNR: 32.507083893
2022-04-24 01:03:53 - Iter[084000], Epoch[000084], learning rate : 0.000327668, Train Loss: 0.264326483, Test MRAE: 0.225298956, Test RMSE: 0.033518806, Test PSNR: 31.669586182
2022-04-24 01:11:12 - Iter[085000], Epoch[000085], learning rate : 0.000326051, Train Loss: 0.263418168, Test MRAE: 0.229342148, Test RMSE: 0.034043733, Test PSNR: 31.438955307
2022-04-24 01:18:32 - Iter[086000], Epoch[000086], learning rate : 0.000324421, Train Loss: 0.262523830, Test MRAE: 0.177279919, Test RMSE: 0.027086122, Test PSNR: 33.416595459
2022-04-24 01:25:51 - Iter[087000], Epoch[000087], learning rate : 0.000322777, Train Loss: 0.261648476, Test MRAE: 0.201754168, Test RMSE: 0.030109784, Test PSNR: 32.651870728
2022-04-24 01:33:11 - Iter[088000], Epoch[000088], learning rate : 0.000321119, Train Loss: 0.260785341, Test MRAE: 0.188595712, Test RMSE: 0.029798815, Test PSNR: 32.890037537
2022-04-24 01:40:29 - Iter[089000], Epoch[000089], learning rate : 0.000319449, Train Loss: 0.259934127, Test MRAE: 0.259095639, Test RMSE: 0.036650077, Test PSNR: 30.832126617
2022-04-24 01:47:48 - Iter[090000], Epoch[000090], learning rate : 0.000317765, Train Loss: 0.259084851, Test MRAE: 0.192954600, Test RMSE: 0.030204322, Test PSNR: 32.897472382
2022-04-24 01:55:07 - Iter[091000], Epoch[000091], learning rate : 0.000316068, Train Loss: 0.258243591, Test MRAE: 0.218330950, Test RMSE: 0.033259917, Test PSNR: 31.820846558
2022-04-24 02:02:26 - Iter[092000], Epoch[000092], learning rate : 0.000314359, Train Loss: 0.257422984, Test MRAE: 0.185128659, Test RMSE: 0.028641550, Test PSNR: 32.873374939
2022-04-24 02:09:45 - Iter[093000], Epoch[000093], learning rate : 0.000312637, Train Loss: 0.256581068, Test MRAE: 0.181253493, Test RMSE: 0.026893286, Test PSNR: 33.463783264
2022-04-24 02:17:05 - Iter[094000], Epoch[000094], learning rate : 0.000310903, Train Loss: 0.255773127, Test MRAE: 0.204837978, Test RMSE: 0.030661592, Test PSNR: 32.446609497
2022-04-24 02:24:25 - Iter[095000], Epoch[000095], learning rate : 0.000309157, Train Loss: 0.254969686, Test MRAE: 0.230381712, Test RMSE: 0.034707155, Test PSNR: 31.355722427
2022-04-24 02:31:44 - Iter[096000], Epoch[000096], learning rate : 0.000307399, Train Loss: 0.254171520, Test MRAE: 0.189732611, Test RMSE: 0.029106423, Test PSNR: 33.067142487
2022-04-24 02:39:03 - Iter[097000], Epoch[000097], learning rate : 0.000305629, Train Loss: 0.253393352, Test MRAE: 0.224733725, Test RMSE: 0.031376496, Test PSNR: 32.192562103
2022-04-24 02:46:22 - Iter[098000], Epoch[000098], learning rate : 0.000303848, Train Loss: 0.252617627, Test MRAE: 0.211100847, Test RMSE: 0.032564305, Test PSNR: 32.234302521
2022-04-24 02:53:43 - Iter[099000], Epoch[000099], learning rate : 0.000302056, Train Loss: 0.187379017, Test MRAE: 0.292518020, Test RMSE: 0.042359829, Test PSNR: 29.537643433
2022-04-24 03:01:02 - Iter[100000], Epoch[000100], learning rate : 0.000300252, Train Loss: 0.174953386, Test MRAE: 0.211716399, Test RMSE: 0.032975309, Test PSNR: 31.872991562
2022-04-24 03:08:21 - Iter[101000], Epoch[000101], learning rate : 0.000298437, Train Loss: 0.175292537, Test MRAE: 0.192068398, Test RMSE: 0.029373646, Test PSNR: 32.905475616
2022-04-24 03:15:39 - Iter[102000], Epoch[000102], learning rate : 0.000296612, Train Loss: 0.174741969, Test MRAE: 0.199211493, Test RMSE: 0.029866356, Test PSNR: 32.346958160
2022-04-24 03:22:57 - Iter[103000], Epoch[000103], learning rate : 0.000294776, Train Loss: 0.174558356, Test MRAE: 0.191865265, Test RMSE: 0.029964963, Test PSNR: 32.781246185
2022-04-24 03:30:15 - Iter[104000], Epoch[000104], learning rate : 0.000292929, Train Loss: 0.174594089, Test MRAE: 0.208459139, Test RMSE: 0.033614278, Test PSNR: 31.671485901
2022-04-24 03:37:34 - Iter[105000], Epoch[000105], learning rate : 0.000291073, Train Loss: 0.174464807, Test MRAE: 0.194269955, Test RMSE: 0.028812120, Test PSNR: 32.986518860
2022-04-24 03:44:53 - Iter[106000], Epoch[000106], learning rate : 0.000289207, Train Loss: 0.173917666, Test MRAE: 0.191931486, Test RMSE: 0.028733607, Test PSNR: 32.865310669
2022-04-24 03:52:11 - Iter[107000], Epoch[000107], learning rate : 0.000287330, Train Loss: 0.173345745, Test MRAE: 0.189958334, Test RMSE: 0.030006712, Test PSNR: 32.687332153
2022-04-24 03:59:29 - Iter[108000], Epoch[000108], learning rate : 0.000285445, Train Loss: 0.173168972, Test MRAE: 0.213803306, Test RMSE: 0.031948101, Test PSNR: 32.112155914
2022-04-24 04:06:47 - Iter[109000], Epoch[000109], learning rate : 0.000283550, Train Loss: 0.172820076, Test MRAE: 0.207145616, Test RMSE: 0.031037169, Test PSNR: 32.362514496
2022-04-24 04:14:05 - Iter[110000], Epoch[000110], learning rate : 0.000281646, Train Loss: 0.172460437, Test MRAE: 0.207781628, Test RMSE: 0.031207027, Test PSNR: 32.349151611
2022-04-24 04:21:23 - Iter[111000], Epoch[000111], learning rate : 0.000279733, Train Loss: 0.172020689, Test MRAE: 0.193833441, Test RMSE: 0.028371841, Test PSNR: 32.973255157
2022-04-24 04:28:42 - Iter[112000], Epoch[000112], learning rate : 0.000277811, Train Loss: 0.171759799, Test MRAE: 0.187842712, Test RMSE: 0.028593259, Test PSNR: 33.276103973
2022-04-24 04:36:00 - Iter[113000], Epoch[000113], learning rate : 0.000275881, Train Loss: 0.171268001, Test MRAE: 0.197044984, Test RMSE: 0.029858861, Test PSNR: 32.776645660
2022-04-24 04:43:18 - Iter[114000], Epoch[000114], learning rate : 0.000273943, Train Loss: 0.170918584, Test MRAE: 0.209609449, Test RMSE: 0.030723853, Test PSNR: 32.419677734
2022-04-24 04:50:37 - Iter[115000], Epoch[000115], learning rate : 0.000271996, Train Loss: 0.170400143, Test MRAE: 0.197612554, Test RMSE: 0.029496051, Test PSNR: 32.795295715
2022-04-24 04:57:55 - Iter[116000], Epoch[000116], learning rate : 0.000270042, Train Loss: 0.170074180, Test MRAE: 0.185922444, Test RMSE: 0.028569115, Test PSNR: 33.193958282
2022-04-24 05:05:14 - Iter[117000], Epoch[000117], learning rate : 0.000268080, Train Loss: 0.169700757, Test MRAE: 0.185285449, Test RMSE: 0.027740482, Test PSNR: 33.480052948
2022-04-24 05:12:33 - Iter[118000], Epoch[000118], learning rate : 0.000266111, Train Loss: 0.169350281, Test MRAE: 0.207038879, Test RMSE: 0.030977149, Test PSNR: 32.194030762
2022-04-24 05:19:51 - Iter[119000], Epoch[000119], learning rate : 0.000264134, Train Loss: 0.168944180, Test MRAE: 0.215334445, Test RMSE: 0.032552138, Test PSNR: 32.056163788
2022-04-24 05:27:09 - Iter[120000], Epoch[000120], learning rate : 0.000262151, Train Loss: 0.168631941, Test MRAE: 0.189310387, Test RMSE: 0.028813055, Test PSNR: 33.156940460
2022-04-24 05:34:28 - Iter[121000], Epoch[000121], learning rate : 0.000260161, Train Loss: 0.168234736, Test MRAE: 0.189293668, Test RMSE: 0.027628548, Test PSNR: 33.350261688
2022-04-24 05:41:46 - Iter[122000], Epoch[000122], learning rate : 0.000258164, Train Loss: 0.167863190, Test MRAE: 0.201189458, Test RMSE: 0.030735698, Test PSNR: 32.294322968
2022-04-24 05:49:05 - Iter[123000], Epoch[000123], learning rate : 0.000256161, Train Loss: 0.167559162, Test MRAE: 0.193951190, Test RMSE: 0.028650727, Test PSNR: 33.052349091
2022-04-24 05:56:23 - Iter[124000], Epoch[000124], learning rate : 0.000254152, Train Loss: 0.167206854, Test MRAE: 0.192059994, Test RMSE: 0.028894641, Test PSNR: 32.890953064
2022-04-24 06:03:42 - Iter[125000], Epoch[000125], learning rate : 0.000252136, Train Loss: 0.166791677, Test MRAE: 0.195547894, Test RMSE: 0.029993979, Test PSNR: 32.774230957
2022-04-24 06:11:00 - Iter[126000], Epoch[000126], learning rate : 0.000250116, Train Loss: 0.166503504, Test MRAE: 0.208153054, Test RMSE: 0.029329335, Test PSNR: 32.743263245
2022-04-24 06:18:18 - Iter[127000], Epoch[000127], learning rate : 0.000248089, Train Loss: 0.166118398, Test MRAE: 0.205973223, Test RMSE: 0.030484146, Test PSNR: 32.552608490
2022-04-24 06:25:36 - Iter[128000], Epoch[000128], learning rate : 0.000246058, Train Loss: 0.165792361, Test MRAE: 0.186198264, Test RMSE: 0.028831014, Test PSNR: 33.019821167
2022-04-24 06:32:55 - Iter[129000], Epoch[000129], learning rate : 0.000244022, Train Loss: 0.165389717, Test MRAE: 0.185266688, Test RMSE: 0.028243279, Test PSNR: 33.310268402
2022-04-24 06:40:13 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.165087610, Test MRAE: 0.171757042, Test RMSE: 0.025719024, Test PSNR: 34.308181763
2022-04-24 06:47:31 - Iter[131000], Epoch[000131], learning rate : 0.000239935, Train Loss: 0.164764866, Test MRAE: 0.182936266, Test RMSE: 0.027655780, Test PSNR: 33.573207855
2022-04-24 06:54:50 - Iter[132000], Epoch[000132], learning rate : 0.000237885, Train Loss: 0.164477885, Test MRAE: 0.195343360, Test RMSE: 0.028816929, Test PSNR: 32.986385345
2022-04-24 07:02:08 - Iter[133000], Epoch[000133], learning rate : 0.000235830, Train Loss: 0.164088041, Test MRAE: 0.186593205, Test RMSE: 0.028469883, Test PSNR: 33.283878326
2022-04-24 07:09:26 - Iter[134000], Epoch[000134], learning rate : 0.000233772, Train Loss: 0.163741812, Test MRAE: 0.192091361, Test RMSE: 0.029141335, Test PSNR: 32.874610901
2022-04-24 07:16:44 - Iter[135000], Epoch[000135], learning rate : 0.000231711, Train Loss: 0.163431987, Test MRAE: 0.201767147, Test RMSE: 0.030382309, Test PSNR: 32.503967285
2022-04-24 07:24:03 - Iter[136000], Epoch[000136], learning rate : 0.000229646, Train Loss: 0.163089812, Test MRAE: 0.190785199, Test RMSE: 0.028191026, Test PSNR: 33.092475891
2022-04-24 07:31:21 - Iter[137000], Epoch[000137], learning rate : 0.000227577, Train Loss: 0.162756190, Test MRAE: 0.200800315, Test RMSE: 0.030721582, Test PSNR: 32.373355865
2022-04-24 07:38:39 - Iter[138000], Epoch[000138], learning rate : 0.000225506, Train Loss: 0.162427396, Test MRAE: 0.187375635, Test RMSE: 0.028447162, Test PSNR: 33.240310669
2022-04-24 07:45:57 - Iter[139000], Epoch[000139], learning rate : 0.000223432, Train Loss: 0.162073910, Test MRAE: 0.183070421, Test RMSE: 0.028210567, Test PSNR: 33.403774261
2022-04-24 07:53:15 - Iter[140000], Epoch[000140], learning rate : 0.000221356, Train Loss: 0.161731496, Test MRAE: 0.206697404, Test RMSE: 0.030781444, Test PSNR: 32.376693726
2022-04-24 08:00:33 - Iter[141000], Epoch[000141], learning rate : 0.000219277, Train Loss: 0.161429659, Test MRAE: 0.180838421, Test RMSE: 0.028606741, Test PSNR: 33.289703369
2022-04-24 08:07:51 - Iter[142000], Epoch[000142], learning rate : 0.000217196, Train Loss: 0.161114916, Test MRAE: 0.192765042, Test RMSE: 0.028382536, Test PSNR: 32.898967743
2022-04-24 08:15:09 - Iter[143000], Epoch[000143], learning rate : 0.000215113, Train Loss: 0.160755455, Test MRAE: 0.185001299, Test RMSE: 0.028346550, Test PSNR: 33.255981445
2022-04-24 08:22:28 - Iter[144000], Epoch[000144], learning rate : 0.000213029, Train Loss: 0.160434559, Test MRAE: 0.213673800, Test RMSE: 0.031143304, Test PSNR: 32.284603119
2022-04-24 08:29:46 - Iter[145000], Epoch[000145], learning rate : 0.000210943, Train Loss: 0.160109028, Test MRAE: 0.186839774, Test RMSE: 0.027673034, Test PSNR: 33.312625885
2022-04-24 08:37:04 - Iter[146000], Epoch[000146], learning rate : 0.000208856, Train Loss: 0.159801781, Test MRAE: 0.196545482, Test RMSE: 0.029323492, Test PSNR: 32.930919647
2022-04-24 08:44:22 - Iter[147000], Epoch[000147], learning rate : 0.000206769, Train Loss: 0.159466505, Test MRAE: 0.179124355, Test RMSE: 0.026816322, Test PSNR: 33.645820618
2022-04-24 08:51:41 - Iter[148000], Epoch[000148], learning rate : 0.000204680, Train Loss: 0.159137771, Test MRAE: 0.181773245, Test RMSE: 0.027678542, Test PSNR: 33.570980072
2022-04-24 08:58:59 - Iter[149000], Epoch[000149], learning rate : 0.000202591, Train Loss: 0.158845395, Test MRAE: 0.204846010, Test RMSE: 0.031462088, Test PSNR: 32.330032349
2022-04-24 09:06:17 - Iter[150000], Epoch[000150], learning rate : 0.000200502, Train Loss: 0.158520535, Test MRAE: 0.185076982, Test RMSE: 0.028418075, Test PSNR: 33.433258057
2022-04-24 09:13:35 - Iter[151000], Epoch[000151], learning rate : 0.000198413, Train Loss: 0.158196121, Test MRAE: 0.188823670, Test RMSE: 0.027936595, Test PSNR: 33.251190186
2022-04-24 09:20:53 - Iter[152000], Epoch[000152], learning rate : 0.000196324, Train Loss: 0.157901078, Test MRAE: 0.190914571, Test RMSE: 0.028032271, Test PSNR: 33.343799591
2022-04-24 09:28:12 - Iter[153000], Epoch[000153], learning rate : 0.000194236, Train Loss: 0.157595128, Test MRAE: 0.195536569, Test RMSE: 0.029458940, Test PSNR: 33.061073303
2022-04-24 09:35:30 - Iter[154000], Epoch[000154], learning rate : 0.000192148, Train Loss: 0.157288760, Test MRAE: 0.201812759, Test RMSE: 0.029618399, Test PSNR: 32.898368835
2022-04-24 09:42:48 - Iter[155000], Epoch[000155], learning rate : 0.000190061, Train Loss: 0.157006115, Test MRAE: 0.182614058, Test RMSE: 0.027506016, Test PSNR: 33.641895294
2022-04-24 09:50:06 - Iter[156000], Epoch[000156], learning rate : 0.000187975, Train Loss: 0.156712040, Test MRAE: 0.195435017, Test RMSE: 0.029001802, Test PSNR: 33.167751312
2022-04-24 09:57:24 - Iter[157000], Epoch[000157], learning rate : 0.000185891, Train Loss: 0.156370819, Test MRAE: 0.198810935, Test RMSE: 0.030198749, Test PSNR: 32.904567719
2022-04-24 10:04:42 - Iter[158000], Epoch[000158], learning rate : 0.000183808, Train Loss: 0.156057492, Test MRAE: 0.193174705, Test RMSE: 0.029364264, Test PSNR: 33.021099091
2022-04-24 10:12:00 - Iter[159000], Epoch[000159], learning rate : 0.000181727, Train Loss: 0.155770376, Test MRAE: 0.190843657, Test RMSE: 0.028110404, Test PSNR: 33.480899811
2022-04-24 10:19:18 - Iter[160000], Epoch[000160], learning rate : 0.000179649, Train Loss: 0.155477762, Test MRAE: 0.183438942, Test RMSE: 0.026869349, Test PSNR: 33.813861847
2022-04-24 10:26:37 - Iter[161000], Epoch[000161], learning rate : 0.000177572, Train Loss: 0.155192718, Test MRAE: 0.191847950, Test RMSE: 0.028657356, Test PSNR: 33.191947937
2022-04-24 10:33:54 - Iter[162000], Epoch[000162], learning rate : 0.000175498, Train Loss: 0.154889181, Test MRAE: 0.193738356, Test RMSE: 0.028951552, Test PSNR: 33.181270599
2022-04-24 10:41:12 - Iter[163000], Epoch[000163], learning rate : 0.000173427, Train Loss: 0.154601350, Test MRAE: 0.181295872, Test RMSE: 0.027756857, Test PSNR: 33.744651794
2022-04-24 10:48:30 - Iter[164000], Epoch[000164], learning rate : 0.000171359, Train Loss: 0.154313490, Test MRAE: 0.194337785, Test RMSE: 0.028127024, Test PSNR: 33.125617981
2022-04-24 10:55:48 - Iter[165000], Epoch[000165], learning rate : 0.000169293, Train Loss: 0.154033646, Test MRAE: 0.198848605, Test RMSE: 0.029735999, Test PSNR: 33.029682159
2022-04-24 11:03:06 - Iter[166000], Epoch[000166], learning rate : 0.000167232, Train Loss: 0.153727159, Test MRAE: 0.198143408, Test RMSE: 0.029354600, Test PSNR: 32.918411255
2022-04-24 11:10:23 - Iter[167000], Epoch[000167], learning rate : 0.000165174, Train Loss: 0.153441325, Test MRAE: 0.202114806, Test RMSE: 0.030279832, Test PSNR: 32.675811768
2022-04-24 11:17:41 - Iter[168000], Epoch[000168], learning rate : 0.000163119, Train Loss: 0.153163671, Test MRAE: 0.196622267, Test RMSE: 0.028848579, Test PSNR: 33.263637543
2022-04-24 11:24:59 - Iter[169000], Epoch[000169], learning rate : 0.000161069, Train Loss: 0.152868360, Test MRAE: 0.200552896, Test RMSE: 0.030392008, Test PSNR: 32.757450104
2022-04-24 11:32:16 - Iter[170000], Epoch[000170], learning rate : 0.000159024, Train Loss: 0.152583852, Test MRAE: 0.191062555, Test RMSE: 0.028261738, Test PSNR: 33.421081543
2022-04-24 11:39:34 - Iter[171000], Epoch[000171], learning rate : 0.000156982, Train Loss: 0.152305588, Test MRAE: 0.188496679, Test RMSE: 0.027919805, Test PSNR: 33.371952057
2022-04-24 11:46:51 - Iter[172000], Epoch[000172], learning rate : 0.000154946, Train Loss: 0.152020514, Test MRAE: 0.197553769, Test RMSE: 0.028797030, Test PSNR: 32.917861938
2022-04-24 11:54:09 - Iter[173000], Epoch[000173], learning rate : 0.000152915, Train Loss: 0.151756734, Test MRAE: 0.187920764, Test RMSE: 0.028187139, Test PSNR: 33.405300140
2022-04-24 12:01:27 - Iter[174000], Epoch[000174], learning rate : 0.000150888, Train Loss: 0.151476368, Test MRAE: 0.187718078, Test RMSE: 0.028445661, Test PSNR: 33.126426697
2022-04-24 12:08:44 - Iter[175000], Epoch[000175], learning rate : 0.000148868, Train Loss: 0.151197165, Test MRAE: 0.190837786, Test RMSE: 0.028355949, Test PSNR: 33.251171112
2022-04-24 12:16:02 - Iter[176000], Epoch[000176], learning rate : 0.000146853, Train Loss: 0.150923252, Test MRAE: 0.194028884, Test RMSE: 0.028654153, Test PSNR: 33.249439240
2022-04-24 12:23:20 - Iter[177000], Epoch[000177], learning rate : 0.000144843, Train Loss: 0.150633112, Test MRAE: 0.194285929, Test RMSE: 0.028783321, Test PSNR: 33.142196655
2022-04-24 12:30:38 - Iter[178000], Epoch[000178], learning rate : 0.000142840, Train Loss: 0.150360137, Test MRAE: 0.191896319, Test RMSE: 0.028720818, Test PSNR: 33.193103790
2022-04-24 12:37:56 - Iter[179000], Epoch[000179], learning rate : 0.000140843, Train Loss: 0.150079399, Test MRAE: 0.185007438, Test RMSE: 0.027334327, Test PSNR: 33.528759003
2022-04-24 12:45:16 - Iter[180000], Epoch[000180], learning rate : 0.000138853, Train Loss: 0.149812862, Test MRAE: 0.176740527, Test RMSE: 0.027778788, Test PSNR: 33.651699066
2022-04-24 12:52:35 - Iter[181000], Epoch[000181], learning rate : 0.000136870, Train Loss: 0.149552971, Test MRAE: 0.180193007, Test RMSE: 0.027510041, Test PSNR: 33.683227539
2022-04-24 12:59:54 - Iter[182000], Epoch[000182], learning rate : 0.000134893, Train Loss: 0.149272114, Test MRAE: 0.195656061, Test RMSE: 0.029211259, Test PSNR: 32.961952209
2022-04-24 13:07:14 - Iter[183000], Epoch[000183], learning rate : 0.000132924, Train Loss: 0.148983032, Test MRAE: 0.197576284, Test RMSE: 0.029444277, Test PSNR: 33.000873566
2022-04-24 13:14:34 - Iter[184000], Epoch[000184], learning rate : 0.000130962, Train Loss: 0.148719370, Test MRAE: 0.194441408, Test RMSE: 0.029059503, Test PSNR: 33.092857361
2022-04-24 13:21:52 - Iter[185000], Epoch[000185], learning rate : 0.000129008, Train Loss: 0.148442432, Test MRAE: 0.188052893, Test RMSE: 0.027879000, Test PSNR: 33.557704926
2022-04-24 13:29:11 - Iter[186000], Epoch[000186], learning rate : 0.000127061, Train Loss: 0.148177505, Test MRAE: 0.196514219, Test RMSE: 0.029250760, Test PSNR: 33.129795074
2022-04-24 13:36:29 - Iter[187000], Epoch[000187], learning rate : 0.000125123, Train Loss: 0.147909477, Test MRAE: 0.191182449, Test RMSE: 0.028239178, Test PSNR: 33.259849548
2022-04-24 13:43:49 - Iter[188000], Epoch[000188], learning rate : 0.000123193, Train Loss: 0.147639051, Test MRAE: 0.182451814, Test RMSE: 0.027355768, Test PSNR: 33.872863770
2022-04-24 13:51:08 - Iter[189000], Epoch[000189], learning rate : 0.000121271, Train Loss: 0.147371769, Test MRAE: 0.202768639, Test RMSE: 0.029921161, Test PSNR: 32.700515747
2022-04-24 13:58:27 - Iter[190000], Epoch[000190], learning rate : 0.000119358, Train Loss: 0.147104174, Test MRAE: 0.191522896, Test RMSE: 0.028326061, Test PSNR: 33.333923340
2022-04-24 14:05:47 - Iter[191000], Epoch[000191], learning rate : 0.000117454, Train Loss: 0.146843359, Test MRAE: 0.192092046, Test RMSE: 0.028346106, Test PSNR: 33.224014282
2022-04-24 14:13:06 - Iter[192000], Epoch[000192], learning rate : 0.000115559, Train Loss: 0.146578267, Test MRAE: 0.193697736, Test RMSE: 0.028687831, Test PSNR: 33.181804657
2022-04-24 14:20:27 - Iter[193000], Epoch[000193], learning rate : 0.000113673, Train Loss: 0.146311790, Test MRAE: 0.205014288, Test RMSE: 0.029817482, Test PSNR: 32.790225983
2022-04-24 14:27:47 - Iter[194000], Epoch[000194], learning rate : 0.000111797, Train Loss: 0.146059215, Test MRAE: 0.191871136, Test RMSE: 0.029173369, Test PSNR: 33.156478882
2022-04-24 14:35:08 - Iter[195000], Epoch[000195], learning rate : 0.000109931, Train Loss: 0.145790294, Test MRAE: 0.194012433, Test RMSE: 0.029836020, Test PSNR: 32.903003693
2022-04-24 14:42:28 - Iter[196000], Epoch[000196], learning rate : 0.000108074, Train Loss: 0.145536378, Test MRAE: 0.198642313, Test RMSE: 0.029362079, Test PSNR: 33.019145966
2022-04-24 14:49:49 - Iter[197000], Epoch[000197], learning rate : 0.000106228, Train Loss: 0.145277962, Test MRAE: 0.186432898, Test RMSE: 0.027287468, Test PSNR: 33.540611267
2022-04-24 14:57:10 - Iter[198000], Epoch[000198], learning rate : 0.000104392, Train Loss: 0.119614117, Test MRAE: 0.194284409, Test RMSE: 0.028485717, Test PSNR: 33.240417480
2022-04-24 15:04:31 - Iter[199000], Epoch[000199], learning rate : 0.000102567, Train Loss: 0.119601652, Test MRAE: 0.199217886, Test RMSE: 0.028936286, Test PSNR: 32.960441589
2022-04-24 15:11:52 - Iter[200000], Epoch[000200], learning rate : 0.000100752, Train Loss: 0.119030163, Test MRAE: 0.190162152, Test RMSE: 0.027372748, Test PSNR: 33.376945496
2022-04-24 15:19:12 - Iter[201000], Epoch[000201], learning rate : 0.000098948, Train Loss: 0.119284987, Test MRAE: 0.186604425, Test RMSE: 0.027210934, Test PSNR: 33.563591003
2022-04-24 15:26:33 - Iter[202000], Epoch[000202], learning rate : 0.000097155, Train Loss: 0.119169325, Test MRAE: 0.193810001, Test RMSE: 0.028337905, Test PSNR: 33.225967407
2022-04-24 15:33:53 - Iter[203000], Epoch[000203], learning rate : 0.000095374, Train Loss: 0.118961580, Test MRAE: 0.194709226, Test RMSE: 0.028918305, Test PSNR: 33.150672913
2022-04-24 15:41:13 - Iter[204000], Epoch[000204], learning rate : 0.000093604, Train Loss: 0.118689805, Test MRAE: 0.192928985, Test RMSE: 0.028235812, Test PSNR: 33.170028687
2022-04-24 15:48:33 - Iter[205000], Epoch[000205], learning rate : 0.000091846, Train Loss: 0.118456222, Test MRAE: 0.193301633, Test RMSE: 0.028252587, Test PSNR: 33.122783661
2022-04-24 15:55:54 - Iter[206000], Epoch[000206], learning rate : 0.000090100, Train Loss: 0.118174106, Test MRAE: 0.185490906, Test RMSE: 0.026967628, Test PSNR: 33.496852875
2022-04-24 16:03:16 - Iter[207000], Epoch[000207], learning rate : 0.000088366, Train Loss: 0.117997423, Test MRAE: 0.187564954, Test RMSE: 0.026877787, Test PSNR: 33.712368011
2022-04-24 16:10:38 - Iter[208000], Epoch[000208], learning rate : 0.000086644, Train Loss: 0.117776223, Test MRAE: 0.195299834, Test RMSE: 0.028869774, Test PSNR: 33.121284485
2022-04-24 16:18:00 - Iter[209000], Epoch[000209], learning rate : 0.000084935, Train Loss: 0.117535874, Test MRAE: 0.191929489, Test RMSE: 0.028113388, Test PSNR: 33.323303223
2022-04-24 16:25:22 - Iter[210000], Epoch[000210], learning rate : 0.000083239, Train Loss: 0.117304310, Test MRAE: 0.194968656, Test RMSE: 0.028367367, Test PSNR: 33.114398956
2022-04-24 16:32:44 - Iter[211000], Epoch[000211], learning rate : 0.000081555, Train Loss: 0.117140234, Test MRAE: 0.195665300, Test RMSE: 0.028352456, Test PSNR: 33.127342224
2022-04-24 16:40:07 - Iter[212000], Epoch[000212], learning rate : 0.000079884, Train Loss: 0.116954863, Test MRAE: 0.195212811, Test RMSE: 0.028834442, Test PSNR: 33.073101044
2022-04-24 16:47:28 - Iter[213000], Epoch[000213], learning rate : 0.000078227, Train Loss: 0.116722025, Test MRAE: 0.198963925, Test RMSE: 0.029507497, Test PSNR: 32.933269501
2022-04-24 16:54:48 - Iter[214000], Epoch[000214], learning rate : 0.000076583, Train Loss: 0.116588168, Test MRAE: 0.197408706, Test RMSE: 0.028881133, Test PSNR: 33.049217224
2022-04-24 17:02:09 - Iter[215000], Epoch[000215], learning rate : 0.000074952, Train Loss: 0.116375208, Test MRAE: 0.198374212, Test RMSE: 0.029086342, Test PSNR: 32.948265076
2022-04-24 17:09:30 - Iter[216000], Epoch[000216], learning rate : 0.000073336, Train Loss: 0.116223402, Test MRAE: 0.196986288, Test RMSE: 0.028938310, Test PSNR: 33.063030243
2022-04-24 17:16:52 - Iter[217000], Epoch[000217], learning rate : 0.000071733, Train Loss: 0.116020106, Test MRAE: 0.195507616, Test RMSE: 0.028648293, Test PSNR: 33.023826599
2022-04-24 17:24:13 - Iter[218000], Epoch[000218], learning rate : 0.000070144, Train Loss: 0.115837477, Test MRAE: 0.194288880, Test RMSE: 0.029007306, Test PSNR: 33.029388428
2022-04-24 17:31:35 - Iter[219000], Epoch[000219], learning rate : 0.000068570, Train Loss: 0.115676008, Test MRAE: 0.187339529, Test RMSE: 0.027608557, Test PSNR: 33.419860840
2022-04-24 17:38:56 - Iter[220000], Epoch[000220], learning rate : 0.000067010, Train Loss: 0.115510687, Test MRAE: 0.184353143, Test RMSE: 0.027777519, Test PSNR: 33.345214844
2022-04-24 17:46:15 - Iter[221000], Epoch[000221], learning rate : 0.000065465, Train Loss: 0.115346827, Test MRAE: 0.195037559, Test RMSE: 0.028969988, Test PSNR: 33.008804321
2022-04-24 17:53:35 - Iter[222000], Epoch[000222], learning rate : 0.000063934, Train Loss: 0.115188368, Test MRAE: 0.193374187, Test RMSE: 0.027930459, Test PSNR: 33.402507782
2022-04-24 18:00:55 - Iter[223000], Epoch[000223], learning rate : 0.000062419, Train Loss: 0.115021795, Test MRAE: 0.182416052, Test RMSE: 0.027217561, Test PSNR: 33.567363739
2022-04-24 18:08:15 - Iter[224000], Epoch[000224], learning rate : 0.000060919, Train Loss: 0.114839152, Test MRAE: 0.191421956, Test RMSE: 0.027706344, Test PSNR: 33.391902924
2022-04-24 18:15:35 - Iter[225000], Epoch[000225], learning rate : 0.000059434, Train Loss: 0.114668220, Test MRAE: 0.192653328, Test RMSE: 0.028672187, Test PSNR: 33.299812317
2022-04-24 18:22:56 - Iter[226000], Epoch[000226], learning rate : 0.000057964, Train Loss: 0.114490494, Test MRAE: 0.189060688, Test RMSE: 0.028604738, Test PSNR: 33.378322601
2022-04-24 18:30:17 - Iter[227000], Epoch[000227], learning rate : 0.000056510, Train Loss: 0.114311807, Test MRAE: 0.196756259, Test RMSE: 0.029171044, Test PSNR: 33.132175446
2022-04-24 18:37:36 - Iter[228000], Epoch[000228], learning rate : 0.000055072, Train Loss: 0.114143245, Test MRAE: 0.193013012, Test RMSE: 0.028449439, Test PSNR: 33.250068665
2022-04-24 18:44:56 - Iter[229000], Epoch[000229], learning rate : 0.000053650, Train Loss: 0.113974586, Test MRAE: 0.190610170, Test RMSE: 0.027618058, Test PSNR: 33.445621490
2022-04-24 18:52:15 - Iter[230000], Epoch[000230], learning rate : 0.000052244, Train Loss: 0.113814212, Test MRAE: 0.196884945, Test RMSE: 0.028659677, Test PSNR: 33.229999542
2022-04-24 18:59:35 - Iter[231000], Epoch[000231], learning rate : 0.000050854, Train Loss: 0.113657691, Test MRAE: 0.197366878, Test RMSE: 0.028392715, Test PSNR: 33.422290802
2022-04-24 19:06:55 - Iter[232000], Epoch[000232], learning rate : 0.000049481, Train Loss: 0.113504767, Test MRAE: 0.195777044, Test RMSE: 0.028548542, Test PSNR: 33.302223206
2022-04-24 19:14:15 - Iter[233000], Epoch[000233], learning rate : 0.000048124, Train Loss: 0.113349751, Test MRAE: 0.193521813, Test RMSE: 0.028111905, Test PSNR: 33.356731415
2022-04-24 19:21:35 - Iter[234000], Epoch[000234], learning rate : 0.000046784, Train Loss: 0.113224901, Test MRAE: 0.194405630, Test RMSE: 0.028365137, Test PSNR: 33.269130707
2022-04-24 19:28:55 - Iter[235000], Epoch[000235], learning rate : 0.000045461, Train Loss: 0.113069437, Test MRAE: 0.198184729, Test RMSE: 0.029390341, Test PSNR: 33.144153595
2022-04-24 19:36:16 - Iter[236000], Epoch[000236], learning rate : 0.000044154, Train Loss: 0.112928368, Test MRAE: 0.191152319, Test RMSE: 0.027889384, Test PSNR: 33.447509766
2022-04-24 19:43:37 - Iter[237000], Epoch[000237], learning rate : 0.000042865, Train Loss: 0.112792470, Test MRAE: 0.197385520, Test RMSE: 0.028791999, Test PSNR: 33.194896698
2022-04-24 19:50:57 - Iter[238000], Epoch[000238], learning rate : 0.000041594, Train Loss: 0.112650365, Test MRAE: 0.192621469, Test RMSE: 0.028283162, Test PSNR: 33.409297943
2022-04-24 19:58:17 - Iter[239000], Epoch[000239], learning rate : 0.000040339, Train Loss: 0.112503625, Test MRAE: 0.193220615, Test RMSE: 0.027964119, Test PSNR: 33.317302704
2022-04-24 20:05:37 - Iter[240000], Epoch[000240], learning rate : 0.000039102, Train Loss: 0.112360999, Test MRAE: 0.194889277, Test RMSE: 0.028867373, Test PSNR: 33.078792572
2022-04-24 20:12:58 - Iter[241000], Epoch[000241], learning rate : 0.000037883, Train Loss: 0.112223759, Test MRAE: 0.195022658, Test RMSE: 0.028741399, Test PSNR: 33.137336731
2022-04-24 20:20:19 - Iter[242000], Epoch[000242], learning rate : 0.000036682, Train Loss: 0.112089776, Test MRAE: 0.192139983, Test RMSE: 0.028171144, Test PSNR: 33.352325439
2022-04-24 20:27:40 - Iter[243000], Epoch[000243], learning rate : 0.000035499, Train Loss: 0.111961752, Test MRAE: 0.192551702, Test RMSE: 0.028077926, Test PSNR: 33.265277863
2022-04-24 20:35:02 - Iter[244000], Epoch[000244], learning rate : 0.000034333, Train Loss: 0.111831270, Test MRAE: 0.188473567, Test RMSE: 0.027880963, Test PSNR: 33.510009766
2022-04-24 20:42:23 - Iter[245000], Epoch[000245], learning rate : 0.000033186, Train Loss: 0.111701354, Test MRAE: 0.190001145, Test RMSE: 0.027866142, Test PSNR: 33.416790009
2022-04-24 20:49:44 - Iter[246000], Epoch[000246], learning rate : 0.000032058, Train Loss: 0.111571535, Test MRAE: 0.195960239, Test RMSE: 0.028473953, Test PSNR: 33.187641144
2022-04-24 20:57:04 - Iter[247000], Epoch[000247], learning rate : 0.000030948, Train Loss: 0.111442573, Test MRAE: 0.194865420, Test RMSE: 0.028670577, Test PSNR: 33.282550812
2022-04-24 21:04:25 - Iter[248000], Epoch[000248], learning rate : 0.000029856, Train Loss: 0.111314408, Test MRAE: 0.193976149, Test RMSE: 0.028541660, Test PSNR: 33.315597534
2022-04-24 21:11:46 - Iter[249000], Epoch[000249], learning rate : 0.000028783, Train Loss: 0.111181580, Test MRAE: 0.194534749, Test RMSE: 0.028667413, Test PSNR: 33.234100342
2022-04-24 21:19:07 - Iter[250000], Epoch[000250], learning rate : 0.000027729, Train Loss: 0.111058399, Test MRAE: 0.191256002, Test RMSE: 0.028059019, Test PSNR: 33.389774323
2022-04-24 21:26:26 - Iter[251000], Epoch[000251], learning rate : 0.000026694, Train Loss: 0.110924609, Test MRAE: 0.191799611, Test RMSE: 0.028243830, Test PSNR: 33.425292969
2022-04-24 21:33:46 - Iter[252000], Epoch[000252], learning rate : 0.000025678, Train Loss: 0.110801347, Test MRAE: 0.198542535, Test RMSE: 0.028921198, Test PSNR: 33.061321259
2022-04-24 21:41:06 - Iter[253000], Epoch[000253], learning rate : 0.000024681, Train Loss: 0.110674061, Test MRAE: 0.191469952, Test RMSE: 0.028229378, Test PSNR: 33.353290558
2022-04-24 21:48:25 - Iter[254000], Epoch[000254], learning rate : 0.000023703, Train Loss: 0.110554203, Test MRAE: 0.196946427, Test RMSE: 0.028728431, Test PSNR: 33.167907715
2022-04-24 21:55:45 - Iter[255000], Epoch[000255], learning rate : 0.000022745, Train Loss: 0.110437430, Test MRAE: 0.200987428, Test RMSE: 0.029347839, Test PSNR: 32.999298096
2022-04-24 22:03:05 - Iter[256000], Epoch[000256], learning rate : 0.000021806, Train Loss: 0.110327788, Test MRAE: 0.195038483, Test RMSE: 0.028396418, Test PSNR: 33.246967316
2022-04-24 22:10:26 - Iter[257000], Epoch[000257], learning rate : 0.000020887, Train Loss: 0.110206954, Test MRAE: 0.192444131, Test RMSE: 0.027873226, Test PSNR: 33.299457550
2022-04-24 22:17:46 - Iter[258000], Epoch[000258], learning rate : 0.000019988, Train Loss: 0.110100083, Test MRAE: 0.189662039, Test RMSE: 0.027660850, Test PSNR: 33.430969238
2022-04-24 22:25:05 - Iter[259000], Epoch[000259], learning rate : 0.000019108, Train Loss: 0.109988384, Test MRAE: 0.192446187, Test RMSE: 0.027883710, Test PSNR: 33.295215607
2022-04-24 22:32:24 - Iter[260000], Epoch[000260], learning rate : 0.000018249, Train Loss: 0.109879568, Test MRAE: 0.191771939, Test RMSE: 0.027974784, Test PSNR: 33.287986755
2022-04-24 22:39:43 - Iter[261000], Epoch[000261], learning rate : 0.000017409, Train Loss: 0.109768525, Test MRAE: 0.193399429, Test RMSE: 0.027999796, Test PSNR: 33.280628204
2022-04-24 22:47:02 - Iter[262000], Epoch[000262], learning rate : 0.000016589, Train Loss: 0.109663777, Test MRAE: 0.193255767, Test RMSE: 0.028116269, Test PSNR: 33.266124725
2022-04-24 22:54:21 - Iter[263000], Epoch[000263], learning rate : 0.000015790, Train Loss: 0.109543800, Test MRAE: 0.190936163, Test RMSE: 0.027700245, Test PSNR: 33.444225311
2022-04-24 23:01:40 - Iter[264000], Epoch[000264], learning rate : 0.000015010, Train Loss: 0.109444395, Test MRAE: 0.192117453, Test RMSE: 0.028006442, Test PSNR: 33.333522797
2022-04-24 23:08:59 - Iter[265000], Epoch[000265], learning rate : 0.000014251, Train Loss: 0.109341353, Test MRAE: 0.191457242, Test RMSE: 0.027899975, Test PSNR: 33.397121429
2022-04-24 23:16:17 - Iter[266000], Epoch[000266], learning rate : 0.000013513, Train Loss: 0.109241709, Test MRAE: 0.192560405, Test RMSE: 0.028087679, Test PSNR: 33.321346283
2022-04-24 23:23:36 - Iter[267000], Epoch[000267], learning rate : 0.000012795, Train Loss: 0.109132715, Test MRAE: 0.189227849, Test RMSE: 0.027522875, Test PSNR: 33.476875305
2022-04-24 23:30:55 - Iter[268000], Epoch[000268], learning rate : 0.000012098, Train Loss: 0.109030411, Test MRAE: 0.195385262, Test RMSE: 0.028207531, Test PSNR: 33.202529907
2022-04-24 23:38:13 - Iter[269000], Epoch[000269], learning rate : 0.000011421, Train Loss: 0.108932234, Test MRAE: 0.197375268, Test RMSE: 0.028543826, Test PSNR: 33.118133545
2022-04-24 23:45:32 - Iter[270000], Epoch[000270], learning rate : 0.000010765, Train Loss: 0.108829290, Test MRAE: 0.194602966, Test RMSE: 0.028211690, Test PSNR: 33.236942291
2022-04-24 23:52:50 - Iter[271000], Epoch[000271], learning rate : 0.000010130, Train Loss: 0.108731277, Test MRAE: 0.192048892, Test RMSE: 0.027873550, Test PSNR: 33.455093384
2022-04-25 00:00:09 - Iter[272000], Epoch[000272], learning rate : 0.000009515, Train Loss: 0.108636267, Test MRAE: 0.193195060, Test RMSE: 0.028050357, Test PSNR: 33.340560913
2022-04-25 00:07:27 - Iter[273000], Epoch[000273], learning rate : 0.000008922, Train Loss: 0.108537480, Test MRAE: 0.194005102, Test RMSE: 0.028298568, Test PSNR: 33.314556122
2022-04-25 00:14:45 - Iter[274000], Epoch[000274], learning rate : 0.000008350, Train Loss: 0.108451046, Test MRAE: 0.192745954, Test RMSE: 0.028208751, Test PSNR: 33.307144165
2022-04-25 00:22:04 - Iter[275000], Epoch[000275], learning rate : 0.000007798, Train Loss: 0.108362779, Test MRAE: 0.193475202, Test RMSE: 0.028108802, Test PSNR: 33.269763947
2022-04-25 00:29:22 - Iter[276000], Epoch[000276], learning rate : 0.000007268, Train Loss: 0.108277828, Test MRAE: 0.193221331, Test RMSE: 0.028187940, Test PSNR: 33.291122437
2022-04-25 00:36:41 - Iter[277000], Epoch[000277], learning rate : 0.000006759, Train Loss: 0.108191997, Test MRAE: 0.192161709, Test RMSE: 0.027926577, Test PSNR: 33.327976227
2022-04-25 00:44:00 - Iter[278000], Epoch[000278], learning rate : 0.000006271, Train Loss: 0.108103476, Test MRAE: 0.190783933, Test RMSE: 0.027791373, Test PSNR: 33.367534637
2022-04-25 00:51:18 - Iter[279000], Epoch[000279], learning rate : 0.000005805, Train Loss: 0.108019732, Test MRAE: 0.191877529, Test RMSE: 0.027891723, Test PSNR: 33.367824554
2022-04-25 00:58:37 - Iter[280000], Epoch[000280], learning rate : 0.000005360, Train Loss: 0.107933961, Test MRAE: 0.195950538, Test RMSE: 0.028457902, Test PSNR: 33.153133392
2022-04-25 01:05:55 - Iter[281000], Epoch[000281], learning rate : 0.000004936, Train Loss: 0.107846662, Test MRAE: 0.192597136, Test RMSE: 0.028107554, Test PSNR: 33.279209137
2022-04-25 01:13:13 - Iter[282000], Epoch[000282], learning rate : 0.000004534, Train Loss: 0.107764423, Test MRAE: 0.191412717, Test RMSE: 0.027916230, Test PSNR: 33.350807190
2022-04-25 01:20:32 - Iter[283000], Epoch[000283], learning rate : 0.000004153, Train Loss: 0.107679963, Test MRAE: 0.191312447, Test RMSE: 0.027765753, Test PSNR: 33.366985321
2022-04-25 01:27:50 - Iter[284000], Epoch[000284], learning rate : 0.000003794, Train Loss: 0.107602857, Test MRAE: 0.191858307, Test RMSE: 0.027884450, Test PSNR: 33.341236115
2022-04-25 01:35:09 - Iter[285000], Epoch[000285], learning rate : 0.000003457, Train Loss: 0.107528247, Test MRAE: 0.192754701, Test RMSE: 0.028111732, Test PSNR: 33.308029175
2022-04-25 01:42:28 - Iter[286000], Epoch[000286], learning rate : 0.000003140, Train Loss: 0.107446305, Test MRAE: 0.190222293, Test RMSE: 0.027751794, Test PSNR: 33.374828339
2022-04-25 01:49:47 - Iter[287000], Epoch[000287], learning rate : 0.000002846, Train Loss: 0.107369162, Test MRAE: 0.191404685, Test RMSE: 0.027805457, Test PSNR: 33.328914642
2022-04-25 01:57:05 - Iter[288000], Epoch[000288], learning rate : 0.000002573, Train Loss: 0.107302025, Test MRAE: 0.191693738, Test RMSE: 0.027790993, Test PSNR: 33.341640472
2022-04-25 02:04:24 - Iter[289000], Epoch[000289], learning rate : 0.000002322, Train Loss: 0.107228436, Test MRAE: 0.191679433, Test RMSE: 0.027835412, Test PSNR: 33.328002930
2022-04-25 02:11:42 - Iter[290000], Epoch[000290], learning rate : 0.000002093, Train Loss: 0.107157536, Test MRAE: 0.192484647, Test RMSE: 0.027921949, Test PSNR: 33.307685852
2022-04-25 02:19:01 - Iter[291000], Epoch[000291], learning rate : 0.000001886, Train Loss: 0.107085161, Test MRAE: 0.193462729, Test RMSE: 0.028064493, Test PSNR: 33.257076263
2022-04-25 02:26:20 - Iter[292000], Epoch[000292], learning rate : 0.000001700, Train Loss: 0.107021876, Test MRAE: 0.193105310, Test RMSE: 0.027996341, Test PSNR: 33.286804199
2022-04-25 02:33:38 - Iter[293000], Epoch[000293], learning rate : 0.000001536, Train Loss: 0.106950894, Test MRAE: 0.193283319, Test RMSE: 0.028045714, Test PSNR: 33.267177582
2022-04-25 02:40:57 - Iter[294000], Epoch[000294], learning rate : 0.000001394, Train Loss: 0.106886789, Test MRAE: 0.193099514, Test RMSE: 0.028048841, Test PSNR: 33.277034760
2022-04-25 02:48:16 - Iter[295000], Epoch[000295], learning rate : 0.000001274, Train Loss: 0.106826186, Test MRAE: 0.192139208, Test RMSE: 0.027863521, Test PSNR: 33.322937012
2022-04-25 02:55:35 - Iter[296000], Epoch[000296], learning rate : 0.000001175, Train Loss: 0.106758937, Test MRAE: 0.192912608, Test RMSE: 0.028000895, Test PSNR: 33.294258118
2022-04-25 03:02:55 - Iter[297000], Epoch[000297], learning rate : 0.000001099, Train Loss: 0.100103371, Test MRAE: 0.193131670, Test RMSE: 0.028011546, Test PSNR: 33.279724121
2022-04-25 03:10:14 - Iter[298000], Epoch[000298], learning rate : 0.000001044, Train Loss: 0.100786686, Test MRAE: 0.193027794, Test RMSE: 0.028003890, Test PSNR: 33.287620544
2022-04-25 03:17:32 - Iter[299000], Epoch[000299], learning rate : 0.000001011, Train Loss: 0.100584567, Test MRAE: 0.192829221, Test RMSE: 0.028046956, Test PSNR: 33.293117523
2022-04-25 03:24:50 - Iter[300000], Epoch[000300], learning rate : 0.000001000, Train Loss: 0.100522660, Test MRAE: 0.192916155, Test RMSE: 0.028030595, Test PSNR: 33.289031982
2022-04-25 03:32:08 - Iter[301000], Epoch[000301], learning rate : 0.000001011, Train Loss: 0.100492030, Test MRAE: 0.192705929, Test RMSE: 0.027994413, Test PSNR: 33.288204193
2022-04-25 03:39:27 - Iter[302000], Epoch[000302], learning rate : 0.000001044, Train Loss: 0.100428887, Test MRAE: 0.193443120, Test RMSE: 0.028072964, Test PSNR: 33.260681152
2022-04-25 03:46:45 - Iter[303000], Epoch[000303], learning rate : 0.000001098, Train Loss: 0.100451186, Test MRAE: 0.192712948, Test RMSE: 0.027910719, Test PSNR: 33.321060181
2022-04-25 03:54:04 - Iter[304000], Epoch[000304], learning rate : 0.000001175, Train Loss: 0.100443088, Test MRAE: 0.192540258, Test RMSE: 0.027973484, Test PSNR: 33.307041168
2022-04-25 04:01:23 - Iter[305000], Epoch[000305], learning rate : 0.000001273, Train Loss: 0.100475624, Test MRAE: 0.192070320, Test RMSE: 0.027928030, Test PSNR: 33.318874359
2022-04-25 04:08:41 - Iter[306000], Epoch[000306], learning rate : 0.000001394, Train Loss: 0.100457005, Test MRAE: 0.192947790, Test RMSE: 0.028051626, Test PSNR: 33.287414551
2022-04-25 04:16:00 - Iter[307000], Epoch[000307], learning rate : 0.000001536, Train Loss: 0.100411482, Test MRAE: 0.192452580, Test RMSE: 0.027951982, Test PSNR: 33.319274902
2022-04-25 04:23:18 - Iter[308000], Epoch[000308], learning rate : 0.000001699, Train Loss: 0.100393593, Test MRAE: 0.192609608, Test RMSE: 0.027955797, Test PSNR: 33.318065643
2022-04-25 04:30:37 - Iter[309000], Epoch[000309], learning rate : 0.000001885, Train Loss: 0.100404568, Test MRAE: 0.193527639, Test RMSE: 0.028155362, Test PSNR: 33.278736115
2022-04-25 04:37:55 - Iter[310000], Epoch[000310], learning rate : 0.000002093, Train Loss: 0.100373536, Test MRAE: 0.193133578, Test RMSE: 0.028036298, Test PSNR: 33.295341492
2022-04-25 04:45:13 - Iter[311000], Epoch[000311], learning rate : 0.000002322, Train Loss: 0.100360446, Test MRAE: 0.192658529, Test RMSE: 0.027874822, Test PSNR: 33.360816956
2022-04-25 04:52:32 - Iter[312000], Epoch[000312], learning rate : 0.000002573, Train Loss: 0.100328170, Test MRAE: 0.193602905, Test RMSE: 0.028098824, Test PSNR: 33.271419525
2022-04-25 04:59:50 - Iter[313000], Epoch[000313], learning rate : 0.000002846, Train Loss: 0.100343518, Test MRAE: 0.193990901, Test RMSE: 0.028186914, Test PSNR: 33.249984741
2022-04-25 05:07:09 - Iter[314000], Epoch[000314], learning rate : 0.000003140, Train Loss: 0.100305840, Test MRAE: 0.192723826, Test RMSE: 0.028080635, Test PSNR: 33.310142517
2022-04-25 05:14:27 - Iter[315000], Epoch[000315], learning rate : 0.000003456, Train Loss: 0.100282602, Test MRAE: 0.193830311, Test RMSE: 0.028120656, Test PSNR: 33.282764435
2022-04-25 05:21:46 - Iter[316000], Epoch[000316], learning rate : 0.000003793, Train Loss: 0.100293078, Test MRAE: 0.193340376, Test RMSE: 0.028024929, Test PSNR: 33.321392059
2022-04-25 05:29:04 - Iter[317000], Epoch[000317], learning rate : 0.000004153, Train Loss: 0.100300558, Test MRAE: 0.194276482, Test RMSE: 0.028109962, Test PSNR: 33.271160126
2022-04-25 05:36:23 - Iter[318000], Epoch[000318], learning rate : 0.000004533, Train Loss: 0.100299679, Test MRAE: 0.193010762, Test RMSE: 0.028052075, Test PSNR: 33.288307190
2022-04-25 05:43:41 - Iter[319000], Epoch[000319], learning rate : 0.000004935, Train Loss: 0.100290351, Test MRAE: 0.193181947, Test RMSE: 0.028065795, Test PSNR: 33.319797516
2022-04-25 05:51:00 - Iter[320000], Epoch[000320], learning rate : 0.000005359, Train Loss: 0.100309148, Test MRAE: 0.193051532, Test RMSE: 0.027957903, Test PSNR: 33.291980743
2022-04-25 05:58:18 - Iter[321000], Epoch[000321], learning rate : 0.000005804, Train Loss: 0.100315474, Test MRAE: 0.193479568, Test RMSE: 0.028086752, Test PSNR: 33.297370911
2022-04-25 06:05:37 - Iter[322000], Epoch[000322], learning rate : 0.000006271, Train Loss: 0.100331835, Test MRAE: 0.194060355, Test RMSE: 0.028113918, Test PSNR: 33.308826447
2022-04-25 06:12:56 - Iter[323000], Epoch[000323], learning rate : 0.000006758, Train Loss: 0.100326367, Test MRAE: 0.192917377, Test RMSE: 0.028060777, Test PSNR: 33.276668549
2022-04-25 06:20:14 - Iter[324000], Epoch[000324], learning rate : 0.000007267, Train Loss: 0.100309573, Test MRAE: 0.192594945, Test RMSE: 0.027995214, Test PSNR: 33.353843689
2022-04-25 06:27:33 - Iter[325000], Epoch[000325], learning rate : 0.000007797, Train Loss: 0.100315861, Test MRAE: 0.192203879, Test RMSE: 0.027706515, Test PSNR: 33.353431702
2022-04-25 06:34:52 - Iter[326000], Epoch[000326], learning rate : 0.000008349, Train Loss: 0.100331157, Test MRAE: 0.191649273, Test RMSE: 0.027642781, Test PSNR: 33.366142273
2022-04-25 06:42:10 - Iter[327000], Epoch[000327], learning rate : 0.000008921, Train Loss: 0.100344434, Test MRAE: 0.190838873, Test RMSE: 0.027811479, Test PSNR: 33.388477325
2022-04-25 06:49:29 - Iter[328000], Epoch[000328], learning rate : 0.000009514, Train Loss: 0.100356624, Test MRAE: 0.196171448, Test RMSE: 0.028467944, Test PSNR: 33.149604797
2022-04-25 06:56:47 - Iter[329000], Epoch[000329], learning rate : 0.000010128, Train Loss: 0.100372978, Test MRAE: 0.194282979, Test RMSE: 0.028084654, Test PSNR: 33.257694244
2022-04-25 07:04:06 - Iter[330000], Epoch[000330], learning rate : 0.000010764, Train Loss: 0.100376308, Test MRAE: 0.192414373, Test RMSE: 0.028048191, Test PSNR: 33.308914185
2022-04-25 07:11:24 - Iter[331000], Epoch[000331], learning rate : 0.000011420, Train Loss: 0.100391522, Test MRAE: 0.190433323, Test RMSE: 0.027647497, Test PSNR: 33.380081177
2022-04-25 07:18:42 - Iter[332000], Epoch[000332], learning rate : 0.000012096, Train Loss: 0.100417353, Test MRAE: 0.192695066, Test RMSE: 0.027837165, Test PSNR: 33.414421082
2022-04-25 07:26:01 - Iter[333000], Epoch[000333], learning rate : 0.000012794, Train Loss: 0.100432314, Test MRAE: 0.191302016, Test RMSE: 0.027844984, Test PSNR: 33.401130676
2022-04-25 07:33:19 - Iter[334000], Epoch[000334], learning rate : 0.000013512, Train Loss: 0.100446559, Test MRAE: 0.191882744, Test RMSE: 0.027901363, Test PSNR: 33.406620026
2022-04-25 07:40:38 - Iter[335000], Epoch[000335], learning rate : 0.000014250, Train Loss: 0.100481153, Test MRAE: 0.192416564, Test RMSE: 0.027720923, Test PSNR: 33.348865509
2022-04-25 07:47:56 - Iter[336000], Epoch[000336], learning rate : 0.000015009, Train Loss: 0.100485310, Test MRAE: 0.195567399, Test RMSE: 0.028326435, Test PSNR: 33.189731598
2022-04-25 07:55:15 - Iter[337000], Epoch[000337], learning rate : 0.000015788, Train Loss: 0.100490183, Test MRAE: 0.191298172, Test RMSE: 0.027739143, Test PSNR: 33.414680481
2022-04-25 08:02:33 - Iter[338000], Epoch[000338], learning rate : 0.000016587, Train Loss: 0.100500144, Test MRAE: 0.191939250, Test RMSE: 0.027749429, Test PSNR: 33.358745575
2022-04-25 08:09:51 - Iter[339000], Epoch[000339], learning rate : 0.000017407, Train Loss: 0.100513048, Test MRAE: 0.193578646, Test RMSE: 0.028436175, Test PSNR: 33.194198608
2022-04-25 08:17:10 - Iter[340000], Epoch[000340], learning rate : 0.000018247, Train Loss: 0.100533307, Test MRAE: 0.188586503, Test RMSE: 0.027242580, Test PSNR: 33.498710632
2022-04-25 08:24:28 - Iter[341000], Epoch[000341], learning rate : 0.000019107, Train Loss: 0.100540973, Test MRAE: 0.192482978, Test RMSE: 0.027988896, Test PSNR: 33.286563873
2022-04-25 08:31:46 - Iter[342000], Epoch[000342], learning rate : 0.000019986, Train Loss: 0.100547455, Test MRAE: 0.193515435, Test RMSE: 0.028014202, Test PSNR: 33.301597595
2022-04-25 08:39:05 - Iter[343000], Epoch[000343], learning rate : 0.000020885, Train Loss: 0.100559235, Test MRAE: 0.189672738, Test RMSE: 0.027482890, Test PSNR: 33.392658234
2022-04-25 08:46:22 - Iter[344000], Epoch[000344], learning rate : 0.000021805, Train Loss: 0.100583985, Test MRAE: 0.191590473, Test RMSE: 0.027853174, Test PSNR: 33.388183594
2022-04-25 08:53:41 - Iter[345000], Epoch[000345], learning rate : 0.000022743, Train Loss: 0.100601755, Test MRAE: 0.194984034, Test RMSE: 0.028497947, Test PSNR: 33.249679565
2022-04-25 09:00:59 - Iter[346000], Epoch[000346], learning rate : 0.000023701, Train Loss: 0.100615494, Test MRAE: 0.197239742, Test RMSE: 0.028750485, Test PSNR: 33.140640259
2022-04-25 09:08:17 - Iter[347000], Epoch[000347], learning rate : 0.000024679, Train Loss: 0.100636765, Test MRAE: 0.196016386, Test RMSE: 0.028450480, Test PSNR: 33.167160034
2022-04-25 09:15:35 - Iter[348000], Epoch[000348], learning rate : 0.000025676, Train Loss: 0.100656524, Test MRAE: 0.197138593, Test RMSE: 0.028332481, Test PSNR: 33.146278381
2022-04-25 09:22:52 - Iter[349000], Epoch[000349], learning rate : 0.000026692, Train Loss: 0.100671567, Test MRAE: 0.195395917, Test RMSE: 0.028719062, Test PSNR: 33.144409180
2022-04-25 09:30:10 - Iter[350000], Epoch[000350], learning rate : 0.000027727, Train Loss: 0.100671843, Test MRAE: 0.190300480, Test RMSE: 0.027716041, Test PSNR: 33.427600861
2022-04-25 09:37:28 - Iter[351000], Epoch[000351], learning rate : 0.000028781, Train Loss: 0.100686379, Test MRAE: 0.191752121, Test RMSE: 0.028139398, Test PSNR: 33.332141876
2022-04-25 09:44:46 - Iter[352000], Epoch[000352], learning rate : 0.000029854, Train Loss: 0.100718208, Test MRAE: 0.194682866, Test RMSE: 0.028210618, Test PSNR: 33.282142639
2022-04-25 09:52:04 - Iter[353000], Epoch[000353], learning rate : 0.000030945, Train Loss: 0.100735955, Test MRAE: 0.197214946, Test RMSE: 0.028641183, Test PSNR: 33.134101868
2022-04-25 09:59:22 - Iter[354000], Epoch[000354], learning rate : 0.000032055, Train Loss: 0.100751780, Test MRAE: 0.197115183, Test RMSE: 0.028570535, Test PSNR: 33.193943024
2022-04-25 10:06:40 - Iter[355000], Epoch[000355], learning rate : 0.000033184, Train Loss: 0.100769475, Test MRAE: 0.192328215, Test RMSE: 0.028011767, Test PSNR: 33.398696899
2022-04-25 10:13:59 - Iter[356000], Epoch[000356], learning rate : 0.000034331, Train Loss: 0.100803539, Test MRAE: 0.196381629, Test RMSE: 0.028617674, Test PSNR: 33.201137543
2022-04-25 10:21:17 - Iter[357000], Epoch[000357], learning rate : 0.000035496, Train Loss: 0.100821510, Test MRAE: 0.196217835, Test RMSE: 0.028774058, Test PSNR: 33.051891327
2022-04-25 10:28:34 - Iter[358000], Epoch[000358], learning rate : 0.000036680, Train Loss: 0.100854121, Test MRAE: 0.199545383, Test RMSE: 0.029118024, Test PSNR: 32.928192139
2022-04-25 10:35:52 - Iter[359000], Epoch[000359], learning rate : 0.000037881, Train Loss: 0.100878023, Test MRAE: 0.193362549, Test RMSE: 0.028006965, Test PSNR: 33.241718292
2022-04-25 10:43:10 - Iter[360000], Epoch[000360], learning rate : 0.000039100, Train Loss: 0.100901194, Test MRAE: 0.194429711, Test RMSE: 0.028516179, Test PSNR: 33.265106201
2022-04-25 10:50:28 - Iter[361000], Epoch[000361], learning rate : 0.000040337, Train Loss: 0.100928769, Test MRAE: 0.189210832, Test RMSE: 0.027448811, Test PSNR: 33.416702271
2022-04-25 10:57:47 - Iter[362000], Epoch[000362], learning rate : 0.000041591, Train Loss: 0.100960627, Test MRAE: 0.193956450, Test RMSE: 0.028303327, Test PSNR: 33.157215118
2022-04-25 11:05:05 - Iter[363000], Epoch[000363], learning rate : 0.000042863, Train Loss: 0.100989930, Test MRAE: 0.186224461, Test RMSE: 0.027105190, Test PSNR: 33.579971313
2022-04-25 11:12:25 - Iter[364000], Epoch[000364], learning rate : 0.000044152, Train Loss: 0.101008587, Test MRAE: 0.195704281, Test RMSE: 0.028517846, Test PSNR: 33.324932098
2022-04-25 11:19:43 - Iter[365000], Epoch[000365], learning rate : 0.000045458, Train Loss: 0.101025164, Test MRAE: 0.192698345, Test RMSE: 0.027865002, Test PSNR: 33.434673309
2022-04-25 11:27:02 - Iter[366000], Epoch[000366], learning rate : 0.000046781, Train Loss: 0.101053961, Test MRAE: 0.192501158, Test RMSE: 0.027623275, Test PSNR: 33.320720673
2022-04-25 11:34:22 - Iter[367000], Epoch[000367], learning rate : 0.000048121, Train Loss: 0.101087205, Test MRAE: 0.198300779, Test RMSE: 0.029013067, Test PSNR: 32.950069427
2022-04-25 11:41:42 - Iter[368000], Epoch[000368], learning rate : 0.000049478, Train Loss: 0.101124302, Test MRAE: 0.196339443, Test RMSE: 0.028887913, Test PSNR: 33.168350220
2022-04-25 11:49:01 - Iter[369000], Epoch[000369], learning rate : 0.000050851, Train Loss: 0.101163298, Test MRAE: 0.191636503, Test RMSE: 0.028039858, Test PSNR: 33.261226654
2022-04-25 11:56:20 - Iter[370000], Epoch[000370], learning rate : 0.000052241, Train Loss: 0.101194531, Test MRAE: 0.201625884, Test RMSE: 0.029340962, Test PSNR: 32.945503235
2022-04-25 12:03:38 - Iter[371000], Epoch[000371], learning rate : 0.000053647, Train Loss: 0.101220258, Test MRAE: 0.193235621, Test RMSE: 0.028013399, Test PSNR: 33.259433746
2022-04-25 12:10:56 - Iter[372000], Epoch[000372], learning rate : 0.000055069, Train Loss: 0.101255767, Test MRAE: 0.193480015, Test RMSE: 0.028273148, Test PSNR: 33.184658051
2022-04-25 12:18:14 - Iter[373000], Epoch[000373], learning rate : 0.000056507, Train Loss: 0.101293430, Test MRAE: 0.192715615, Test RMSE: 0.028238066, Test PSNR: 33.218509674
2022-04-25 12:25:32 - Iter[374000], Epoch[000374], learning rate : 0.000057961, Train Loss: 0.101333998, Test MRAE: 0.195869312, Test RMSE: 0.028549308, Test PSNR: 33.319480896
2022-04-25 12:32:50 - Iter[375000], Epoch[000375], learning rate : 0.000059431, Train Loss: 0.101367302, Test MRAE: 0.192657486, Test RMSE: 0.028557653, Test PSNR: 33.174678802
2022-04-25 12:40:09 - Iter[376000], Epoch[000376], learning rate : 0.000060916, Train Loss: 0.101408660, Test MRAE: 0.203487262, Test RMSE: 0.029706266, Test PSNR: 32.700252533
2022-04-25 12:47:27 - Iter[377000], Epoch[000377], learning rate : 0.000062416, Train Loss: 0.101447403, Test MRAE: 0.190485701, Test RMSE: 0.027895560, Test PSNR: 33.485603333
2022-04-25 12:54:46 - Iter[378000], Epoch[000378], learning rate : 0.000063931, Train Loss: 0.101491779, Test MRAE: 0.202929333, Test RMSE: 0.029769374, Test PSNR: 32.721004486
2022-04-25 13:02:04 - Iter[379000], Epoch[000379], learning rate : 0.000065462, Train Loss: 0.101534717, Test MRAE: 0.183758467, Test RMSE: 0.026680194, Test PSNR: 33.685981750
2022-04-25 13:09:24 - Iter[380000], Epoch[000380], learning rate : 0.000067007, Train Loss: 0.101577103, Test MRAE: 0.202234969, Test RMSE: 0.029345138, Test PSNR: 32.879959106
2022-04-25 13:16:43 - Iter[381000], Epoch[000381], learning rate : 0.000068567, Train Loss: 0.101620190, Test MRAE: 0.202066645, Test RMSE: 0.028961357, Test PSNR: 32.886264801
2022-04-25 13:24:02 - Iter[382000], Epoch[000382], learning rate : 0.000070141, Train Loss: 0.101662815, Test MRAE: 0.192256540, Test RMSE: 0.028235797, Test PSNR: 33.233028412
2022-04-25 13:31:21 - Iter[383000], Epoch[000383], learning rate : 0.000071730, Train Loss: 0.101709768, Test MRAE: 0.187021822, Test RMSE: 0.027496511, Test PSNR: 33.441703796
2022-04-25 13:38:43 - Iter[384000], Epoch[000384], learning rate : 0.000073332, Train Loss: 0.101751879, Test MRAE: 0.186171263, Test RMSE: 0.027307799, Test PSNR: 33.580806732
2022-04-25 13:46:02 - Iter[385000], Epoch[000385], learning rate : 0.000074949, Train Loss: 0.101796478, Test MRAE: 0.190495685, Test RMSE: 0.028171303, Test PSNR: 33.406867981
2022-04-25 13:53:21 - Iter[386000], Epoch[000386], learning rate : 0.000076579, Train Loss: 0.101846151, Test MRAE: 0.194317982, Test RMSE: 0.028138859, Test PSNR: 33.285106659
2022-04-25 14:00:40 - Iter[387000], Epoch[000387], learning rate : 0.000078223, Train Loss: 0.101894036, Test MRAE: 0.198126733, Test RMSE: 0.028461866, Test PSNR: 33.141490936
2022-04-25 14:08:00 - Iter[388000], Epoch[000388], learning rate : 0.000079881, Train Loss: 0.101939812, Test MRAE: 0.190160587, Test RMSE: 0.028086560, Test PSNR: 33.183956146
2022-04-25 14:15:22 - Iter[389000], Epoch[000389], learning rate : 0.000081551, Train Loss: 0.101995423, Test MRAE: 0.196040630, Test RMSE: 0.028694905, Test PSNR: 33.369514465
2022-04-25 14:22:42 - Iter[390000], Epoch[000390], learning rate : 0.000083235, Train Loss: 0.102042235, Test MRAE: 0.187708467, Test RMSE: 0.027381109, Test PSNR: 33.268215179
2022-04-25 14:30:04 - Iter[391000], Epoch[000391], learning rate : 0.000084932, Train Loss: 0.102100983, Test MRAE: 0.203920364, Test RMSE: 0.029569706, Test PSNR: 32.845363617
2022-04-25 14:37:29 - Iter[392000], Epoch[000392], learning rate : 0.000086641, Train Loss: 0.102149516, Test MRAE: 0.197997972, Test RMSE: 0.028776843, Test PSNR: 33.276451111
2022-04-25 14:44:51 - Iter[393000], Epoch[000393], learning rate : 0.000088363, Train Loss: 0.102202222, Test MRAE: 0.194265708, Test RMSE: 0.028056512, Test PSNR: 33.470447540
2022-04-25 14:52:11 - Iter[394000], Epoch[000394], learning rate : 0.000090097, Train Loss: 0.102258205, Test MRAE: 0.204532653, Test RMSE: 0.029456845, Test PSNR: 32.766719818
2022-04-25 14:59:33 - Iter[395000], Epoch[000395], learning rate : 0.000091843, Train Loss: 0.102325581, Test MRAE: 0.203758344, Test RMSE: 0.029374652, Test PSNR: 32.788539886
非常感谢大佬的指点！
你的第130个epoch明明到了34.31 dB 了呀，没问题的
2022-04-24 06:40:13 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.165087610, Test MRAE: 0.171757042, Test RMSE: 0.025719024, Test PSNR: 34.308181763
你看我们又给你传数据，还帮你debug。
你都不愿意star，fork一遍我们的repo？
你看我们又给你传数据，还帮你debug。 你都不愿意star，fork一遍我们的repo？
哥，我错了
请问对于您说到的有问题的数据
ARAD_1K_0314：原先的mat文件损坏，我们之前是把它去掉，不过数据集官方更换了一个mat文件。
ARAD_1K_0340：mat文件存在负数
ARAD_1K_0531：mat文件存在负数
是怎么处理的啊，直接删掉嘛
你好，我们已经在MST-plus-plus/dataset/split_txt/train_list.txt中去掉这三个文件了，你不需要专门处理~
你的第130个epoch明明到了34.31 dB 了呀，没问题的
2022-04-24 06:40:13 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.165087610, Test MRAE: 0.171757042, Test RMSE: 0.025719024, Test PSNR: 34.308181763
我这边跑了两次您的开源代码，也是无法Retrain出和您一样的结果，甚至没有一个epoch的Test MRAE达到了0.16的数量级，想问下作者您在跑模型的时候也有这么大的随机性吗
你的第130个epoch明明到了34.31 dB 了呀，没问题的
2022-04-24 06:40:13 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.165087610, Test MRAE: 0.171757042, Test RMSE: 0.025719024, Test PSNR: 34.308181763我这边跑了两次您的开源代码，也是无法Retrain出和您一样的结果，甚至没有一个epoch的Test MRAE达到了0.16的数量级，想问下作者您在跑模型的时候也有这么大的随机性吗
你看我发的 log 里面的这一条
2022-04-02 19:55:01 - Iter[224000], Epoch[000224], learning rate : 0.000060919, Train Loss: 0.099466614, Test MRAE: 0.164563686, Test RMSE: 0.024763588, Test PSNR: 34.316574097
我基本上每一次都可以跑到0.16。我们是用 3090 24G 跑的。显卡不同可能跑出来结果有差别。
你的第130个epoch明明到了34.31 dB 了呀，没问题的
2022-04-24 06:40:13 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.165087610, Test MRAE: 0.171757042, Test RMSE: 0.025719024, Test PSNR: 34.308181763我这边跑了两次您的开源代码，也是无法Retrain出和您一样的结果，甚至没有一个epoch的Test MRAE达到了0.16的数量级，想问下作者您在跑模型的时候也有这么大的随机性吗
你看我发的 log 里面的这一条
2022-04-02 19:55:01 - Iter[224000], Epoch[000224], learning rate : 0.000060919, Train Loss: 0.099466614, Test MRAE: 0.164563686, Test RMSE: 0.024763588, Test PSNR: 34.316574097
我基本上每一次都可以跑到0.16。我们是用 3090 24G 跑的。显卡不同可能跑出来结果有差别。
你的第130个epoch明明到了34.31 dB 了呀，没问题的
2022-04-24 06:40:13 - Iter[130000], Epoch[000130], learning rate : 0.000241980, Train Loss: 0.165087610, Test MRAE: 0.171757042, Test RMSE: 0.025719024, Test PSNR: 34.308181763我这边跑了两次您的开源代码，也是无法Retrain出和您一样的结果，甚至没有一个epoch的Test MRAE达到了0.16的数量级，想问下作者您在跑模型的时候也有这么大的随机性吗
你看我发的 log 里面的这一条
2022-04-02 19:55:01 - Iter[224000], Epoch[000224], learning rate : 0.000060919, Train Loss: 0.099466614, Test MRAE: 0.164563686, Test RMSE: 0.024763588, Test PSNR: 34.316574097
我基本上每一次都可以跑到0.16。我们是用 3090 24G 跑的。显卡不同可能跑出来结果有差别。
我也是3090跑的，不过pytorch用的1.9.0版本