search

caiyuanhao1998 / MST-plus-plus

"MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction" (CVPRW 2022) & (Winner of NTIRE 2022 Spectral Recovery Challenge) and a toolbox for spectral reconstruction
https://arxiv.org/abs/2204.07908
MIT License
417 stars 58 forks source link

Loss very quickly reduces but PSNR and RMSE doesn't improve #15

Closed zaidilyas89 closed 2 years ago

zaidilyas89 commented 2 years ago

Hi authors!

I appreciate your work done. It's quite inspiring!

I am trying to train MST++ from scratch. I just followed the commands mentioned but even after training for long time my RMSE, MRAE and PSNR doesn't improve. I have even tried using recommended environment settings. I am using same settings (i.e. batch size, lr scheduling etc.).

2022-08-23 10:03:27 - Iter[001000], Epoch[000001], learning rate : 0.000399989, Train Loss: 0.345733970, Test MRAE: 5.343356609, Test RMSE: 0.567134500, Test PSNR: 16.002279282 2022-08-23 10:20:02 - Iter[002000], Epoch[000002], learning rate : 0.000399956, Train Loss: 0.242199719, Test MRAE: 1.445869207, Test RMSE: 0.086180650, Test PSNR: 22.718479156 2022-08-23 10:36:37 - Iter[003000], Epoch[000003], learning rate : 0.000399902, Train Loss: 0.192822084, Test MRAE: 1.584116817, Test RMSE: 0.092025109, Test PSNR: 22.301456451 2022-08-23 10:53:12 - Iter[004000], Epoch[000004], learning rate : 0.000399825, Train Loss: 0.180765674, Test MRAE: 0.614064872, Test RMSE: 0.070524208, Test PSNR: 25.304067612 2022-08-23 11:09:47 - Iter[005000], Epoch[000005], learning rate : 0.000399727, Train Loss: 0.175371751, Test MRAE: 0.542492449, Test RMSE: 0.088396654, Test PSNR: 23.954675674 2022-08-23 11:26:22 - Iter[006000], Epoch[000006], learning rate : 0.000399606, Train Loss: 0.175960690, Test MRAE: 1.136486292, Test RMSE: 0.074130528, Test PSNR: 24.200265884 2022-08-23 11:42:57 - Iter[007000], Epoch[000007], learning rate : 0.000399464, Train Loss: 0.179268226, Test MRAE: 1.133757234, Test RMSE: 0.074117400, Test PSNR: 24.215122223 2022-08-23 11:59:33 - Iter[008000], Epoch[000008], learning rate : 0.000399301, Train Loss: 0.183103830, Test MRAE: 0.690664709, Test RMSE: 0.125816882, Test PSNR: 20.855083466 2022-08-23 12:16:08 - Iter[009000], Epoch[000009], learning rate : 0.000399115, Train Loss: 0.180019125, Test MRAE: 0.625649035, Test RMSE: 0.117592424, Test PSNR: 21.640802383 2022-08-23 12:32:44 - Iter[010000], Epoch[000010], learning rate : 0.000398907, Train Loss: 0.175567344, Test MRAE: 0.705689073, Test RMSE: 0.063903458, Test PSNR: 26.012899399 2022-08-23 12:49:19 - Iter[011000], Epoch[000011], learning rate : 0.000398678, Train Loss: 0.173624143, Test MRAE: 0.719805300, Test RMSE: 0.071538091, Test PSNR: 25.122806549 2022-08-23 13:05:55 - Iter[012000], Epoch[000012], learning rate : 0.000398427, Train Loss: 0.167626292, Test MRAE: 1.066743374, Test RMSE: 0.067167260, Test PSNR: 25.091667175 2022-08-23 13:22:30 - Iter[013000], Epoch[000013], learning rate : 0.000398154, Train Loss: 0.159311280, Test MRAE: 1.099061131, Test RMSE: 0.075757533, Test PSNR: 24.224294662 2022-08-23 13:39:06 - Iter[014000], Epoch[000014], learning rate : 0.000397860, Train Loss: 0.158835545, Test MRAE: 0.538660467, Test RMSE: 0.094124049, Test PSNR: 23.704330444 2022-08-23 13:55:43 - Iter[015000], Epoch[000015], learning rate : 0.000397544, Train Loss: 0.159253776, Test MRAE: 0.668565333, Test RMSE: 0.091043182, Test PSNR: 23.187345505 2022-08-23 15:38:40 - Iter[001000], Epoch[000001], learning rate : 0.000399989, Train Loss: 0.127697811, Test MRAE: 0.745330155, Test RMSE: 0.062896006, Test PSNR: 25.861749649 2022-08-23 15:48:46 - Iter[002000], Epoch[000002], learning rate : 0.000399956, Train Loss: 0.115349077, Test MRAE: 1.359989882, Test RMSE: 0.084344082, Test PSNR: 23.167493820 2022-08-23 16:01:59 - Iter[003000], Epoch[000003], learning rate : 0.000399902, Train Loss: 0.103145719, Test MRAE: 1.378445268, Test RMSE: 0.090944812, Test PSNR: 22.637052536 2022-08-23 16:16:06 - Iter[004000], Epoch[000004], learning rate : 0.000399825, Train Loss: 0.111022204, Test MRAE: 0.556447327, Test RMSE: 0.065509431, Test PSNR: 26.170299530 2022-08-23 16:30:18 - Iter[005000], Epoch[000005], learning rate : 0.000399727, Train Loss: 0.115231283, Test MRAE: 0.518974543, Test RMSE: 0.084985338, Test PSNR: 24.737178802 2022-08-23 16:44:30 - Iter[006000], Epoch[000006], learning rate : 0.000399606, Train Loss: 0.132773608, Test MRAE: 1.072223663, Test RMSE: 0.074294783, Test PSNR: 24.498712540 2022-08-23 16:55:09 - Iter[007000], Epoch[000007], learning rate : 0.000399464, Train Loss: 0.140846550, Test MRAE: 1.058269382, Test RMSE: 0.073174037, Test PSNR: 24.492546082

What am I doing wrong?

I would appreciate if you could help me.

Regards!

caiyuanhao1998 commented 2 years ago

Hi, this issue may be caused by the differences of pytorch versions. In the function of Loss_PSNR(), torch.nn is applied. Different versions of pytorch handle the Singular value differently.

My suggestions:

(1) change your pytorch version to torch==1.7.0 or 1.8.0, and test again.

(2-1) If you do not want to change your pytorch. You can also use numpy to calculate PSNR. More specifically, copy the following code to the script test_develop_code/utils.py

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

(2-2) Replace Line 54 ~ 69 of the script test_develop_code/test.py by the following code and test again.

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

Hope this will help you.

zaidilyas89 commented 2 years ago

Hi thank you for your prompt response.

Ok. I will check PSNR formula.

But regarding loss, I used MRAE as used in your work but contrary to your results during training, my loss immediately reduces to 0.2 whereas in your case it gradually decreases.

RMSE also doesn't improve.

This is strange.

Could you kindly guide in this regard please?

Does this also has something to do with pytorch version?

On Wed, Aug 24, 2022, 9:44 PM Yuanhao Cai @.***> wrote:

Hi, this issue may be caused by the differences of pytorch versions. In the function of Loss_PSNR(), torch.nn is applied. Different versions of pytorch handle the Singular value differently.

My suggestions:

(1) change your pytorch version to torch==1.7.0 or 1.8.0, and test again.

(2-1) If you do not want to change your pytorch. You can also use numpy to calculate PSNR. More specifically, copy the following code to the script test_develop_code/utils.py

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

(2-2) Replace Line 54 ~ 69 of the script test_develop_code/test.py by the following code and test again.

from utils import calc_psnr with torch.no_grad():

compute output

if method=='awan':   # To avoid out of memory, we crop the center region as input for AWAN.
    output = model(input[:, :, 118:-118, 118:-118])
    loss_mrae = criterion_mrae(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
    loss_rmse = criterion_rmse(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
    loss_psnr = calc_psnr(output[:, :, 10:-10, 10:-10], target[:, :, 128:-128, 128:-128])
else:
    output = model(input)
    loss_mrae = criterion_mrae(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
    loss_rmse = criterion_rmse(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])
    loss_psnr = calc_psnr(output[:, :, 128:-128, 128:-128], target[:, :, 128:-128, 128:-128])

record loss

losses_mrae.update(loss_mrae.data) losses_rmse.update(loss_rmse.data) losses_psnr.update(loss_psnr)

Hope this will help you.

— Reply to this email directly, view it on GitHub https://github.com/caiyuanhao1998/MST-plus-plus/issues/15#issuecomment-1225748010, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATLF3WSOECCWQMWSKQBSIG3V2YRMJANCNFSM57PLE3YQ . You are receiving this because you authored the thread.Message ID: @.***>

caiyuanhao1998 commented 2 years ago

We have not encountered this problem. Other netizens who use our code do not seem to have encountered this problem, either. We provide the entire training log of MST++ here for your convenience to debug.

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
zaidilyas89 commented 2 years ago

Ok, thanks. I will check.

On Wed, Aug 24, 2022, 9:59 PM Yuanhao Cai @.***> wrote:

We have not encountered this problem. Other netizens who use our code do not seem to have encountered this problem, either. We provide the entire training log of MST++ here for your convenience to debug.

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

— Reply to this email directly, view it on GitHub https://github.com/caiyuanhao1998/MST-plus-plus/issues/15#issuecomment-1225766072, or unsubscribe https://github.com/notifications/unsubscribe-auth/ATLF3WXLIQP3W2XLETWVOKTV2YTC3ANCNFSM57PLE3YQ . You are receiving this because you authored the thread.Message ID: @.***>

zaidilyas89 commented 2 years ago

Just figured out a strange behaviour. The same code was creating problem on one particular machine but when I re ran that code on a different machine then it started to run as expected.

caiyuanhao1998 commented 2 years ago

OK, good luck