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@ProGamerGov
Created October 24, 2017 06:26
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-image_size 2432 & -tv_weight 0.0000005
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:537] Reading dangerously large protocol message. If the message turns out to be larger than 1073741824 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 538683157
Successfully loaded models/VGG16_SOD_finetune.caffemodel
conv1_1: 64 3 3 3
conv1_2: 64 64 3 3
conv2_1: 128 64 3 3
conv2_2: 128 128 3 3
conv3_1: 256 128 3 3
conv3_2: 256 256 3 3
conv3_3: 256 256 3 3
conv4_1: 512 256 3 3
conv4_2: 512 512 3 3
conv4_3: 512 512 3 3
conv5_1: 512 512 3 3
conv5_2: 512 512 3 3
conv5_3: 512 512 3 3
fc6: 1 1 25088 4096
fc7: 1 1 4096 4096
fc8-SOD100: 1 1 4096 100
Setting up style layer 2 : relu1_1
Setting up style layer 7 : relu2_1
Setting up style layer 12 : relu3_1
Setting up style layer 19 : relu4_1
Setting up content layer 21 : relu4_2
Setting up style layer 26 : relu5_1
Capturing content targets
nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> output]
(1): nn.GPU(1) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.TVLoss
(2): cudnn.SpatialConvolution(3 -> 64, 3x3, 1,1, 1,1)
(3): cudnn.ReLU
}
(2): nn.GPU(2) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.StyleLoss
(2): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
(3): cudnn.ReLU
}
(3): nn.GPU(3) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
(1): cudnn.SpatialMaxPooling(2x2, 2,2)
(2): cudnn.SpatialConvolution(64 -> 128, 3x3, 1,1, 1,1)
(3): cudnn.ReLU
(4): nn.StyleLoss
(5): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
(6): cudnn.ReLU
}
(4): nn.GPU(4) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): cudnn.SpatialMaxPooling(2x2, 2,2)
(2): cudnn.SpatialConvolution(128 -> 256, 3x3, 1,1, 1,1)
(3): cudnn.ReLU
}
(5): nn.GPU(5) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
(1): nn.StyleLoss
(2): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
(3): cudnn.ReLU
(4): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
(5): cudnn.ReLU
}
(6): nn.GPU(6) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
(1): cudnn.SpatialMaxPooling(2x2, 2,2)
(2): cudnn.SpatialConvolution(256 -> 512, 3x3, 1,1, 1,1)
(3): cudnn.ReLU
(4): nn.StyleLoss
(5): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
(6): cudnn.ReLU
}
(7): nn.GPU(7) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
(1): nn.ContentLoss
(2): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
(3): cudnn.ReLU
(4): cudnn.SpatialMaxPooling(2x2, 2,2)
(5): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
}
(8): nn.GPU(8) @ nn.Sequential {
[input -> (1) -> (2) -> output]
(1): cudnn.ReLU
(2): nn.StyleLoss
}
}
gram updateOutput value: 0
gram updateOutput value 2: 1164591616
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gram updateOutput value 2: 14068302848
gram updateOutput value: 0
gram updateOutput value 2: 4332710400
gram updateOutput value: 0
gram updateOutput value 2: 274163968
gram updateOutput value: 0
gram updateOutput value 2: 3434939.25
Capturing style target 1
gram updateOutput value: 3.3136463165283
gram updateOutput value 2: 231200432
gram updateOutput value: 80.057891845703
gram updateOutput value 2: 3753993984
gram updateOutput value: 49.26831817627
gram updateOutput value 2: 1826164992
gram updateOutput value: 6.2241559028625
gram updateOutput value 2: 176804336
gram updateOutput value: 0.31082594394684
gram updateOutput value 2: 8545173
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gram updateOutput value: 3.8834133148193
gram updateOutput value 2: 1278823680
gram updateOutput value: 125.94509124756
gram updateOutput value 2: 20267821056
gram updateOutput value: 122.21520233154
gram updateOutput value 2: 10460779520
gram updateOutput value: 23.665088653564
gram updateOutput value 2: 918054848
gram updateOutput value: 4.5750517845154
gram updateOutput value 2: 40656444
y value: 41.496166229248
dy value: 0
Running optimization with ADAM
---
x1 value: -7.1936044692993
gram updateOutput value: 3.6386742591858
gram updateOutput value 2: 1278823680
gram updateOutput value: 115.33726501465
gram updateOutput value 2: 20267821056
gram updateOutput value: 118.95206451416
gram updateOutput value 2: 10460779520
gram updateOutput value: 20.841968536377
gram updateOutput value 2: 918054848
gram updateOutput value: 3.6789810657501
gram updateOutput value 2: 40656444
gram 1 value: -1.6226302079758e-08
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gram 3 value: -5.2896833580007e-08
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gram 1 value: -3.1889019203035e-10
gram 2 value: -6.377803840607e-10
gram 3 value: -6.377803840607e-10
feval(x) grad value: -5.4058352083863e-16
---
StyleLoss:updateOutput input 1: 13.897933006287
StyleLoss:updateOutput output 1: 13.897933006287
StyleLoss:updateGradInput input 1: 13.897933006287
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput input 1: 112.44744110107
StyleLoss:updateOutput output 1: 112.44744110107
StyleLoss:updateGradInput input 1: 112.44744110107
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275219362229e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput input 1: 163.86224365234
StyleLoss:updateOutput output 1: 163.86224365234
StyleLoss:updateGradInput input 1: 163.86224365234
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput input 1: 98.696151733398
StyleLoss:updateOutput output 1: 98.696151733398
StyleLoss:updateGradInput input 1: 98.696151733398
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput input 1: 41.496166229248
StyleLoss:updateOutput output 1: 41.496166229248
StyleLoss:updateGradInput input 1: 41.496166229248
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 1 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
optim value: -7.1467981338501
---
x1 value: -7.1467981338501
gram updateOutput value: 3.6386742591858
gram updateOutput value 2: 1206750720
gram updateOutput value: 115.33726501465
gram updateOutput value 2: 18687930368
gram updateOutput value: 118.95206451416
gram updateOutput value 2: 10190614528
gram updateOutput value: 20.841968536377
gram updateOutput value 2: 848677632
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gram updateOutput value 2: 36033176
gram 1 value: -2.2734282012493e-08
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gram 3 value: -4.5468564024986e-08
gram 1 value: -4.2220587914699e-08
gram 2 value: -8.4441175829397e-08
gram 3 value: -8.4441175829397e-08
gram 1 value: -4.0018566238587e-08
gram 2 value: -8.0037132477173e-08
gram 3 value: -8.0037132477173e-08
gram 1 value: -2.0692647240139e-07
gram 2 value: -4.1385294480278e-07
gram 3 value: -4.1385294480278e-07
gram 1 value: -6.0429472537038e-10
gram 2 value: -1.2085894507408e-09
gram 3 value: -1.2085894507408e-09
feval(x) grad value: 1.2719613189254e-16
---
StyleLoss:updateOutput input 1: 13.516628265381
StyleLoss:updateOutput output 1: 13.516628265381
StyleLoss:updateGradInput input 1: 13.516628265381
StyleLoss:updateOutput self.G 1: 1206750720
StyleLoss:updateOutput self.G 2: 3.4336030483246
StyleLoss:updateGradInput self.gradInput 1: -2.7389350787388e-09
StyleLoss:updateGradInput self.gradInput 2: -1.6433616110589e-05
dG 1: -0.00021963387553114
dG 2: -6.2493066277369e-13
---
StyleLoss:updateOutput input 1: 107.77638244629
StyleLoss:updateOutput output 1: 107.77638244629
StyleLoss:updateGradInput input 1: 107.77638244629
StyleLoss:updateOutput self.G 1: 18687930368
StyleLoss:updateOutput self.G 2: 106.34661865234
StyleLoss:updateGradInput self.gradInput 1: -5.4425308704253e-09
StyleLoss:updateGradInput self.gradInput 2: -4.731443914352e-05
dG 1: -0.0023923912085593
dG 2: -1.361428157709e-11
---
StyleLoss:updateOutput input 1: 161.76884460449
StyleLoss:updateOutput output 1: 161.76884460449
StyleLoss:updateGradInput input 1: 161.76884460449
StyleLoss:updateOutput self.G 1: 10190614528
StyleLoss:updateOutput self.G 2: 115.87995147705
StyleLoss:updateGradInput self.gradInput 1: -1.1233610708317e-09
StyleLoss:updateGradInput self.gradInput 2: -3.9050432860677e-06
dG 1: -0.00019333629461471
dG 2: -2.1984746922249e-12
---
StyleLoss:updateOutput input 1: 94.707298278809
StyleLoss:updateOutput output 1: 94.707298278809
StyleLoss:updateGradInput input 1: 94.707298278809
StyleLoss:updateOutput self.G 1: 848677632
StyleLoss:updateOutput self.G 2: 19.266941070557
StyleLoss:updateGradInput self.gradInput 1: -1.4315402196985e-08
StyleLoss:updateGradInput self.gradInput 2: -9.0013003500644e-05
dG 1: -3.3555243135197e-05
dG 2: -7.6178137149024e-13
---
StyleLoss:updateOutput input 1: 38.940925598145
StyleLoss:updateOutput output 1: 38.940925598145
StyleLoss:updateGradInput input 1: 38.940925598145
StyleLoss:updateOutput self.G 1: 36033176
StyleLoss:updateOutput self.G 2: 3.2606241703033
StyleLoss:updateGradInput self.gradInput 1: -8.2274240753577e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00049364555161446
dG 1: -1.0028291399067e-05
dG 2: -9.0745488380561e-13
---
Iteration 2 / 2500
Content 1 loss: 1836153.125000
Style 1 loss: 4946.664691
Style 2 loss: 5943945.556641
Style 3 loss: 14918696.777344
Style 4 loss: 957293.609619
Style 5 loss: 35902.750969
Total loss: 23696938.484263
optim value: -7.0969209671021
---
x1 value: -7.0969209671021
gram updateOutput value: 3.4336030483246
gram updateOutput value 2: 1149958016
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gram updateOutput value 2: 32482132
gram 1 value: -2.6926239016234e-08
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gram 3 value: -5.3852478032468e-08
gram 1 value: -5.123561308551e-08
gram 2 value: -1.0247122617102e-07
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gram 2 value: -1.638143842797e-09
gram 3 value: -1.638143842797e-09
feval(x) grad value: -7.4374400266066e-16
---
StyleLoss:updateOutput input 1: 13.220321655273
StyleLoss:updateOutput output 1: 13.220321655273
StyleLoss:updateGradInput input 1: 13.220321655273
StyleLoss:updateOutput self.G 1: 1149958016
StyleLoss:updateOutput self.G 2: 3.2720093727112
StyleLoss:updateGradInput self.gradInput 1: -2.80233436456e-09
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dG 1: -0.0002985370811075
dG 2: -8.4943624399994e-13
---
StyleLoss:updateOutput input 1: 104.15986633301
StyleLoss:updateOutput output 1: 104.15986633301
StyleLoss:updateGradInput input 1: 104.15986633301
StyleLoss:updateOutput self.G 1: 17448140800
StyleLoss:updateOutput self.G 2: 99.291412353516
StyleLoss:updateGradInput self.gradInput 1: -5.5571653945208e-09
StyleLoss:updateGradInput self.gradInput 2: -4.818100205739e-05
dG 1: -0.0032536243088543
dG 2: -1.851526224006e-11
---
StyleLoss:updateOutput input 1: 158.92091369629
StyleLoss:updateOutput output 1: 158.92091369629
StyleLoss:updateGradInput input 1: 158.92091369629
StyleLoss:updateOutput self.G 1: 9847486464
StyleLoss:updateOutput self.G 2: 111.97816467285
StyleLoss:updateGradInput self.gradInput 1: -1.728834408965e-09
StyleLoss:updateGradInput self.gradInput 2: -7.7461854743888e-06
dG 1: -0.00031240910175256
dG 2: -3.552480358493e-12
---
StyleLoss:updateOutput input 1: 91.413589477539
StyleLoss:updateOutput output 1: 91.413589477539
StyleLoss:updateGradInput input 1: 91.413589477539
StyleLoss:updateOutput self.G 1: 792051584
StyleLoss:updateOutput self.G 2: 17.981395721436
StyleLoss:updateGradInput self.gradInput 1: -1.5619729509808e-08
StyleLoss:updateGradInput self.gradInput 2: -9.841605060501e-05
dG 1: -4.3363157601561e-05
dG 2: -9.8444386323338e-13
---
StyleLoss:updateOutput input 1: 36.902168273926
StyleLoss:updateOutput output 1: 36.902168273926
StyleLoss:updateGradInput input 1: 36.902168273926
StyleLoss:updateOutput self.G 1: 32482132
StyleLoss:updateOutput self.G 2: 2.9392919540405
StyleLoss:updateGradInput self.gradInput 1: -8.6073562499678e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00051644124323502
dG 1: -1.2479858924053e-05
dG 2: -1.1292959839829e-12
---
Iteration 3 / 2500
Content 1 loss: 1712206.250000
Style 1 loss: 7596.604586
Style 2 loss: 9342404.296875
Style 3 loss: 19629184.570312
Style 4 loss: 1247745.300293
Style 5 loss: 46477.229118
Total loss: 31985614.251184
optim value: -7.0450959205627
---
x1 value: -7.0450959205627
gram updateOutput value: 3.2720093727112
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gram 1 value: -9.9321839641675e-10
gram 2 value: -1.9864367928335e-09
gram 3 value: -1.9864367928335e-09
feval(x) grad value: 3.886548143622e-17
---
StyleLoss:updateOutput input 1: 12.968382835388
StyleLoss:updateOutput output 1: 12.968382835388
StyleLoss:updateGradInput input 1: 12.968382835388
StyleLoss:updateOutput self.G 1: 1102386176
StyleLoss:updateOutput self.G 2: 3.136650800705
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---
StyleLoss:updateOutput input 1: 101.11451721191
StyleLoss:updateOutput output 1: 101.11451721191
StyleLoss:updateGradInput input 1: 101.11451721191
StyleLoss:updateOutput self.G 1: 16412239872
StyleLoss:updateOutput self.G 2: 93.396453857422
StyleLoss:updateGradInput self.gradInput 1: -5.5836606449589e-09
StyleLoss:updateGradInput self.gradInput 2: -4.7892241127556e-05
dG 1: -0.0039732223376632
dG 2: -2.2610252212174e-11
---
StyleLoss:updateOutput input 1: 155.73225402832
StyleLoss:updateOutput output 1: 155.73225402832
StyleLoss:updateGradInput input 1: 155.73225402832
StyleLoss:updateOutput self.G 1: 9472453632
StyleLoss:updateOutput self.G 2: 107.71360015869
StyleLoss:updateGradInput self.gradInput 1: -2.506162832816e-09
StyleLoss:updateGradInput self.gradInput 2: -1.2939052794536e-05
dG 1: -0.00044255357352085
dG 2: -5.0323850989131e-12
---
StyleLoss:updateOutput input 1: 88.531539916992
StyleLoss:updateOutput output 1: 88.531539916992
StyleLoss:updateGradInput input 1: 88.531539916992
StyleLoss:updateOutput self.G 1: 742949696
StyleLoss:updateOutput self.G 2: 16.866670608521
StyleLoss:updateGradInput self.gradInput 1: -1.6470332653284e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00010403496708022
dG 1: -5.1867857109755e-05
dG 2: -1.1775199955724e-12
---
StyleLoss:updateOutput input 1: 35.148273468018
StyleLoss:updateOutput output 1: 35.148273468018
StyleLoss:updateGradInput input 1: 35.148273468018
StyleLoss:updateOutput self.G 1: 29534870
StyleLoss:updateOutput self.G 2: 2.672595500946
StyleLoss:updateGradInput self.gradInput 1: -8.7867228160121e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00052720337407663
dG 1: -1.4514591384795e-05
dG 2: -1.31341801584e-12
---
Iteration 4 / 2500
Content 1 loss: 1606906.347656
Style 1 loss: 10405.622005
Style 2 loss: 12803192.871094
Style 3 loss: 23790433.593750
Style 4 loss: 1529300.170898
Style 5 loss: 56922.489166
Total loss: 39797161.094570
optim value: -6.9919424057007
---
x1 value: -6.9919424057007
gram updateOutput value: 3.136650800705
gram updateOutput value 2: 1061940224
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gram updateOutput value 2: 9084126208
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gram 2 value: -1.2667084092755e-07
gram 3 value: -1.2667084092755e-07
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gram 3 value: -7.1606251594858e-07
gram 1 value: -1.1373710862017e-09
gram 2 value: -2.2747421724034e-09
gram 3 value: -2.2747421724034e-09
feval(x) grad value: 4.2222048674807e-16
---
StyleLoss:updateOutput input 1: 12.749844551086
StyleLoss:updateOutput output 1: 12.749844551086
StyleLoss:updateGradInput input 1: 12.749844551086
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---
Iteration 5 / 2500
Content 1 loss: 1516414.062500
Style 1 loss: 13214.175224
Style 2 loss: 16159595.214844
Style 3 loss: 27307517.578125
Style 4 loss: 1796307.495117
Style 5 loss: 66935.050964
Total loss: 46859983.576775
optim value: -6.9378447532654
---
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gram updateOutput value: 3.0215694904327
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feval(x) grad value: 1.5369531941937e-16
---
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StyleLoss:updateOutput output 1: 32.205951690674
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dG 2: -1.605324597212e-12
---
Iteration 6 / 2500
Content 1 loss: 1435329.003906
Style 1 loss: 15952.959538
Style 2 loss: 19403862.304688
Style 3 loss: 30468416.015625
Style 4 loss: 2056525.634766
Style 5 loss: 76599.214554
Total loss: 53456685.133076
optim value: -6.8831324577332
---
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gram updateOutput value: 2.9220662117004
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feval(x) grad value: -1.5192869985898e-16
---
StyleLoss:updateOutput input 1: 12.382091522217
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StyleLoss:updateOutput output 1: 30.932281494141
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dG 1: -1.9053390133195e-05
dG 2: -1.7241314712729e-12
---
Iteration 7 / 2500
Content 1 loss: 1362586.621094
Style 1 loss: 18586.707115
Style 2 loss: 22502178.222656
Style 3 loss: 33375020.507812
Style 4 loss: 2304691.040039
Style 5 loss: 85755.580902
Total loss: 59648818.679619
optim value: -6.8280878067017
---
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gram updateOutput value: 2.8349840641022
gram updateOutput value 2: 969341376
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feval(x) grad value: -2.2082661125155e-16
---
StyleLoss:updateOutput input 1: 12.224289894104
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dG 2: -1.7452853398603e-12
---
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StyleLoss:updateOutput output 1: 29.786548614502
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dG 1: -2.0194020180497e-05
dG 2: -1.8273462170509e-12
---
Iteration 8 / 2500
Content 1 loss: 1297584.277344
Style 1 loss: 21098.050117
Style 2 loss: 25427961.914062
Style 3 loss: 36071830.078125
Style 4 loss: 2537404.541016
Style 5 loss: 94260.807037
Total loss: 65450139.667702
optim value: -6.7729783058167
---
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gram updateOutput value: 2.7580952644348
gram updateOutput value 2: 945304064
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feval(x) grad value: -1.0069693113082e-16
---
StyleLoss:updateOutput input 1: 12.080185890198
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---
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dG 2: -1.9184786138188e-12
---
Iteration 9 / 2500
Content 1 loss: 1239320.507812
Style 1 loss: 23480.419636
Style 2 loss: 28179823.242188
Style 3 loss: 38604585.937500
Style 4 loss: 2753712.158203
Style 5 loss: 102224.716187
Total loss: 70903146.981525
optim value: -6.7180557250977
---
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gram updateOutput value: 2.6897013187408
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feval(x) grad value: -1.5899515163079e-16
---
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dG 2: -1.9995921151511e-12
---
Iteration 10 / 2500
Content 1 loss: 1187294.433594
Style 1 loss: 25735.945702
Style 2 loss: 30762506.835938
Style 3 loss: 41001043.945312
Style 4 loss: 2953463.195801
Style 5 loss: 109676.891327
Total loss: 76039721.247673
optim value: -6.6635041236877
---
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gram updateOutput value: 2.6284363269806
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gram updateOutput value 2: 7119417344
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feval(x) grad value: 2.1376015947974e-16
---
StyleLoss:updateOutput input 1: 11.825624465942
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StyleLoss:updateOutput output 1: 74.622146606445
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StyleLoss:updateOutput output 1: 26.900667190552
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dG 1: -2.2901624106453e-05
dG 2: -2.0723561755531e-12
---
Iteration 11 / 2500
Content 1 loss: 1140776.562500
Style 1 loss: 27869.084358
Style 2 loss: 33182176.757812
Style 3 loss: 43272093.750000
Style 4 loss: 3136776.123047
Style 5 loss: 116632.564545
Total loss: 80876324.842262
optim value: -6.6094737052917
---
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gram updateOutput value: 2.5732533931732
gram updateOutput value 2: 886823488
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feval(x) grad value: 7.7730962872441e-17
---
StyleLoss:updateOutput input 1: 11.71245098114
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dG 2: -4.1211686840903e-11
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StyleLoss:updateOutput output 1: 132.4069519043
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StyleLoss:updateGradInput self.gradInput 2: -4.8050798795884e-05
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dG 2: -1.5301130154577e-11
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StyleLoss:updateOutput output 1: 73.183242797852
StyleLoss:updateGradInput input 1: 73.183242797852
StyleLoss:updateOutput self.G 1: 504722720
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StyleLoss:updateGradInput self.gradInput 2: -0.00011551659554243
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dG 2: -2.1142631916049e-12
---
StyleLoss:updateOutput input 1: 26.082305908203
StyleLoss:updateOutput output 1: 26.082305908203
StyleLoss:updateGradInput input 1: 26.082305908203
StyleLoss:updateOutput self.G 1: 16341784
StyleLoss:updateOutput self.G 2: 1.4787596464157
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StyleLoss:updateGradInput self.gradInput 2: -0.00054111157078296
dG 1: -2.3622838853044e-05
dG 2: -2.1376184242833e-12
---
Iteration 12 / 2500
Content 1 loss: 1098976.171875
Style 1 loss: 29885.725021
Style 2 loss: 35446807.617188
Style 3 loss: 45423539.062500
Style 4 loss: 3304604.370117
Style 5 loss: 123096.805573
Total loss: 85426909.752274
optim value: -6.5561037063599
---
x1 value: -6.5561037063599
gram updateOutput value: 2.5233047008514
gram updateOutput value 2: 870866560
gram updateOutput value: 66.618713378906
gram updateOutput value 2: 11360462848
gram updateOutput value: 78.122611999512
gram updateOutput value 2: 6638599168
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gram updateOutput value 2: 15394633
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gram 2 value: -3.5570446588196e-09
gram 3 value: -3.5570446588196e-09
feval(x) grad value: 2.6499192489918e-17
---
StyleLoss:updateOutput input 1: 11.607266426086
StyleLoss:updateOutput output 1: 11.607266426086
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dG 1: -0.00068628485314548
dG 2: -1.9527064427233e-12
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dG 2: -4.2580310805063e-11
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StyleLoss:updateOutput output 1: 130.21752929688
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StyleLoss:updateGradInput self.gradInput 2: -5.0318209105171e-05
dG 1: -0.0014259656891227
dG 2: -1.6215005033127e-11
---
StyleLoss:updateOutput input 1: 71.841957092285
StyleLoss:updateOutput output 1: 71.841957092285
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StyleLoss:updateGradInput self.gradInput 2: -0.0001160152387456
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dG 2: -2.1866872462056e-12
---
StyleLoss:updateOutput input 1: 25.317356109619
StyleLoss:updateOutput output 1: 25.317356109619
StyleLoss:updateGradInput input 1: 25.317356109619
StyleLoss:updateOutput self.G 1: 15394633
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StyleLoss:updateGradInput self.gradInput 2: -0.00054124381858855
dG 1: -2.4276727344841e-05
dG 2: -2.1967885410062e-12
---
Iteration 13 / 2500
Content 1 loss: 1061147.265625
Style 1 loss: 31790.943146
Style 2 loss: 37565865.234375
Style 3 loss: 47468531.250000
Style 4 loss: 3458041.992188
Style 5 loss: 129133.289337
Total loss: 89714509.974670
optim value: -6.5035138130188
---
x1 value: -6.5035138130188
gram updateOutput value: 2.4779016971588
gram updateOutput value 2: 856299264
gram updateOutput value: 64.64852142334
gram updateOutput value 2: 11045510144
gram updateOutput value: 75.489143371582
gram updateOutput value 2: 6423248896
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gram updateOutput value 2: 469381856
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gram updateOutput value 2: 14537055
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gram 3 value: -3.6507965539556e-09
feval(x) grad value: 8.8330647147598e-18
---
StyleLoss:updateOutput input 1: 11.509203910828
StyleLoss:updateOutput output 1: 11.509203910828
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dG 2: -2.0102916727105e-12
---
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StyleLoss:updateOutput output 1: 83.846366882324
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dG 2: -4.3825335721559e-11
---
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StyleLoss:updateOutput output 1: 128.15167236328
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dG 2: -1.7064802695921e-11
---
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StyleLoss:updateOutput output 1: 70.584342956543
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StyleLoss:updateGradInput self.gradInput 2: -0.00011652324610623
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dG 2: -2.2532292871802e-12
---
StyleLoss:updateOutput input 1: 24.603151321411
StyleLoss:updateOutput output 1: 24.603151321411
StyleLoss:updateGradInput input 1: 24.603151321411
StyleLoss:updateOutput self.G 1: 14537055
StyleLoss:updateOutput self.G 2: 1.315450668335
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StyleLoss:updateGradInput self.gradInput 2: -0.00054135458776727
dG 1: -2.4868782929843e-05
dG 2: -2.2503637408383e-12
---
Iteration 14 / 2500
Content 1 loss: 1026697.558594
Style 1 loss: 33593.639374
Style 2 loss: 39548853.515625
Style 3 loss: 49419070.312500
Style 4 loss: 3598629.272461
Style 5 loss: 134744.922638
Total loss: 93761589.221191
optim value: -6.4518103599548
---
x1 value: -6.4518103599548
gram updateOutput value: 2.4364531040192
gram updateOutput value 2: 842950720
gram updateOutput value: 62.856224060059
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gram updateOutput value 2: 6222419968
gram updateOutput value: 10.656049728394
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gram updateOutput value: 1.315450668335
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gram 3 value: -3.7363778737642e-09
feval(x) grad value: 7.9497575815393e-17
---
StyleLoss:updateOutput input 1: 11.417538642883
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dG 2: -4.4961520617726e-11
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dG 2: -1.7857288564516e-11
---
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StyleLoss:updateOutput output 1: 69.397804260254
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---
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StyleLoss:updateOutput output 1: 23.929559707642
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StyleLoss:updateGradInput self.gradInput 2: -0.0005414558108896
dG 1: -2.5410459784325e-05
dG 2: -2.2993790031733e-12
---
Iteration 15 / 2500
Content 1 loss: 995027.148438
Style 1 loss: 35301.195145
Style 2 loss: 41404995.117188
Style 3 loss: 51284742.187500
Style 4 loss: 3728646.972656
Style 5 loss: 140000.541687
Total loss: 97588713.162613
optim value: -6.4010877609253
---
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gram updateOutput value: 2.3984723091125
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feval(x) grad value: -1.748946747348e-16
---
StyleLoss:updateOutput input 1: 11.331592559814
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dG 2: -2.3445121712096e-12
---
Iteration 16 / 2500
Content 1 loss: 965745.312500
Style 1 loss: 36921.426773
Style 2 loss: 43143770.507812
Style 3 loss: 53070949.218750
Style 4 loss: 3849214.965820
Style 5 loss: 144936.985016
Total loss: 101211538.416672
optim value: -6.3514180183411
---
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gram updateOutput value: 2.3635196685791
gram updateOutput value 2: 819315776
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feval(x) grad value: -1.695948292885e-16
---
StyleLoss:updateOutput input 1: 11.250781059265
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StyleLoss:updateOutput output 1: 67.190292358398
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StyleLoss:updateOutput output 1: 22.681156158447
StyleLoss:updateGradInput input 1: 22.681156158447
StyleLoss:updateOutput self.G 1: 12353885
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StyleLoss:updateGradInput self.gradInput 2: -0.00054163573076949
dG 1: -2.6375999368611e-05
dG 2: -2.3867507362857e-12
---
Iteration 17 / 2500
Content 1 loss: 938576.660156
Style 1 loss: 38463.214874
Style 2 loss: 44775679.687500
Style 3 loss: 54782296.875000
Style 4 loss: 3961156.860352
Style 5 loss: 149631.889343
Total loss: 104645805.187225
optim value: -6.3028678894043
---
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gram updateOutput value: 2.3312230110168
gram updateOutput value 2: 808788864
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feval(x) grad value: -1.2012968012073e-16
---
StyleLoss:updateOutput input 1: 11.174562454224
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dG 2: -4.7840568312418e-11
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StyleLoss:updateOutput output 1: 120.90824127197
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StyleLoss:updateGradInput self.gradInput 2: -5.7971250498667e-05
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dG 2: -1.993816917123e-11
---
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StyleLoss:updateOutput output 1: 66.15446472168
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dG 2: -2.4772352138352e-12
---
StyleLoss:updateOutput input 1: 22.095590591431
StyleLoss:updateOutput output 1: 22.095590591431
StyleLoss:updateGradInput input 1: 22.095590591431
StyleLoss:updateOutput self.G 1: 11723042
StyleLoss:updateOutput self.G 2: 1.0608117580414
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StyleLoss:updateGradInput self.gradInput 2: -0.000541718211025
dG 1: -2.6811523639481e-05
dG 2: -2.4261610515747e-12
---
Iteration 18 / 2500
Content 1 loss: 913189.843750
Style 1 loss: 39932.756424
Style 2 loss: 46310583.984375
Style 3 loss: 56425822.265625
Style 4 loss: 4065719.604492
Style 5 loss: 154078.868866
Total loss: 107909327.323532
optim value: -6.2554860115051
---
x1 value: -6.2554860115051
gram updateOutput value: 2.3012704849243
gram updateOutput value 2: 798983296
gram updateOutput value: 57.076084136963
gram updateOutput value 2: 9823035392
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gram updateOutput value 2: 5541367296
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gram 3 value: -4.0165817338789e-09
feval(x) grad value: 4.9465159093932e-17
---
StyleLoss:updateOutput input 1: 11.102458953857
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dG 1: -0.00078615383245051
dG 2: -2.2368660743122e-12
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dG 1: -0.0085504837334156
dG 2: -4.8657876339231e-11
---
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StyleLoss:updateOutput output 1: 119.31902313232
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StyleLoss:updateGradInput self.gradInput 2: -5.8982630434912e-05
dG 1: -0.0018067306373268
dG 2: -2.0544770745756e-11
---
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StyleLoss:updateOutput output 1: 65.164520263672
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StyleLoss:updateGradInput self.gradInput 2: -0.00011885028652614
dG 1: -0.00011122751311632
dG 2: -2.5251218217087e-12
---
StyleLoss:updateOutput input 1: 21.537351608276
StyleLoss:updateOutput output 1: 21.537351608276
StyleLoss:updateGradInput input 1: 21.537351608276
StyleLoss:updateOutput self.G 1: 11137034
StyleLoss:updateOutput self.G 2: 1.0077843666077
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StyleLoss:updateGradInput self.gradInput 2: -0.0005417893989943
dG 1: -2.7216090529691e-05
dG 2: -2.4627702221308e-12
---
Iteration 19 / 2500
Content 1 loss: 889410.742188
Style 1 loss: 41336.531639
Style 2 loss: 47757228.515625
Style 3 loss: 58008439.453125
Style 4 loss: 4163928.955078
Style 5 loss: 158272.087097
Total loss: 111018616.284752
optim value: -6.2093071937561
---
x1 value: -6.2093071937561
gram updateOutput value: 2.2733702659607
gram updateOutput value 2: 789817856
gram updateOutput value: 55.899532318115
gram updateOutput value 2: 9630915584
gram updateOutput value: 63.012252807617
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gram 2 value: -4.0747165641619e-09
gram 3 value: -4.0747165641619e-09
feval(x) grad value: 7.7730962872441e-17
---
StyleLoss:updateOutput input 1: 11.034057617188
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StyleLoss:updateGradInput input 1: 11.034057617188
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dG 1: -0.00079888757318258
dG 2: -2.2730979425123e-12
---
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dG 2: -4.9417338277014e-11
---
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dG 1: -0.0018567546503618
dG 2: -2.1113607390211e-11
---
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StyleLoss:updateOutput output 1: 64.214660644531
StyleLoss:updateGradInput input 1: 64.214660644531
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dG 2: -2.5702964062679e-12
---
StyleLoss:updateOutput input 1: 21.006223678589
StyleLoss:updateOutput output 1: 21.006223678589
StyleLoss:updateGradInput input 1: 21.006223678589
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StyleLoss:updateGradInput self.gradInput 2: -0.00054184574401006
dG 1: -2.7590296667768e-05
dG 2: -2.4966320243819e-12
---
Iteration 20 / 2500
Content 1 loss: 867157.128906
Style 1 loss: 42681.930542
Style 2 loss: 49123400.390625
Style 3 loss: 59536957.031250
Style 4 loss: 4256465.698242
Style 5 loss: 162216.693878
Total loss: 113988878.873444
optim value: -6.1643576622009
---
x1 value: -6.1643576622009
gram updateOutput value: 2.2472915649414
gram updateOutput value 2: 781219136
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gram updateOutput value 2: 9451826176
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gram updateOutput value 2: 5261818880
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gram 3 value: -4.1291996488724e-09
feval(x) grad value: 1.2542951233214e-16
---
StyleLoss:updateOutput input 1: 10.969004631042
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dG 2: -2.3070891985028e-12
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dG 2: -5.0125292805347e-11
---
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---
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---
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StyleLoss:updateOutput output 1: 20.500417709351
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dG 1: -2.7937425329583e-05
dG 2: -2.5280426623614e-12
---
Iteration 21 / 2500
Content 1 loss: 846257.226562
Style 1 loss: 43974.987030
Style 2 loss: 50416242.187500
Style 3 loss: 61013255.859375
Style 4 loss: 4344143.554688
Style 5 loss: 165926.582336
Total loss: 116829800.397491
optim value: -6.1206593513489
---
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gram updateOutput value: 2.2228255271912
gram updateOutput value 2: 773130816
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feval(x) grad value: 1.4486226132206e-16
---
StyleLoss:updateOutput input 1: 10.907011032104
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dG 2: -2.5572402268664e-12
---
Iteration 22 / 2500
Content 1 loss: 826661.816406
Style 1 loss: 45217.723846
Style 2 loss: 51642480.468750
Style 3 loss: 62440201.171875
Style 4 loss: 4427393.188477
Style 5 loss: 169417.613983
Total loss: 119551371.983337
optim value: -6.0782251358032
---
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gram updateOutput value: 2.1998119354248
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feval(x) grad value: -9.0097253473105e-17
---
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StyleLoss:updateOutput output 1: 19.555618286133
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dG 2: -2.5844211753306e-12
---
Iteration 23 / 2500
Content 1 loss: 808283.593750
Style 1 loss: 46416.512489
Style 2 loss: 52807429.687500
Style 3 loss: 63822580.078125
Style 4 loss: 4506585.937500
Style 5 loss: 172708.145142
Total loss: 122164003.954506
optim value: -6.0370650291443
---
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gram updateOutput value: 2.1780984401703
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feval(x) grad value: -6.7131291832174e-17
---
StyleLoss:updateOutput input 1: 10.79128742218
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dG 2: -2.3074700136738e-11
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StyleLoss:updateOutput output 1: 60.717037200928
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dG 2: -2.7304374039527e-12
---
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StyleLoss:updateOutput output 1: 19.117782592773
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StyleLoss:updateGradInput self.gradInput 2: -0.00054196390556172
dG 1: -2.8838536309195e-05
dG 2: -2.6095842067114e-12
---
Iteration 24 / 2500
Content 1 loss: 791028.515625
Style 1 loss: 47572.594643
Style 2 loss: 53915882.812500
Style 3 loss: 65162894.531250
Style 4 loss: 4581937.866211
Style 5 loss: 175783.607483
Total loss: 124675099.927711
optim value: -5.9971776008606
---
x1 value: -5.9971776008606
gram updateOutput value: 2.157564163208
gram updateOutput value 2: 751448128
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feval(x) grad value: -1.1129660878853e-16
---
StyleLoss:updateOutput input 1: 10.73715877533
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dG 2: -2.42477630856e-12
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dG 2: -5.2540152783997e-11
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dG 2: -2.3499088092072e-11
---
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dG 2: -2.7657294857097e-12
---
StyleLoss:updateOutput input 1: 18.700895309448
StyleLoss:updateOutput output 1: 18.700895309448
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StyleLoss:updateGradInput self.gradInput 2: -0.00054197548888624
dG 1: -2.9097578590154e-05
dG 2: -2.6330255250423e-12
---
Iteration 25 / 2500
Content 1 loss: 774765.576172
Style 1 loss: 48686.439514
Style 2 loss: 54971794.921875
Style 3 loss: 66463376.953125
Style 4 loss: 4653438.720703
Style 5 loss: 178667.587280
Total loss: 127090730.198669
optim value: -5.9585447311401
---
x1 value: -5.9585447311401
gram updateOutput value: 2.1381168365479
gram updateOutput value 2: 744956544
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feval(x) grad value: 1.9079418460392e-16
---
StyleLoss:updateOutput input 1: 10.685289382935
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---
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dG 2: -2.3901618531608e-11
---
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StyleLoss:updateOutput output 1: 59.140773773193
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---
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StyleLoss:updateOutput output 1: 18.300271987915
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dG 1: -2.9340790206334e-05
dG 2: -2.6550330944203e-12
---
Iteration 26 / 2500
Content 1 loss: 759345.947266
Style 1 loss: 49766.046524
Style 2 loss: 55978623.046875
Style 3 loss: 67725263.671875
Style 4 loss: 4721594.604492
Style 5 loss: 181387.664795
Total loss: 129415980.981827
optim value: -5.9211397171021
---
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gram updateOutput value: 2.1196463108063
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feval(x) grad value: -3.7098871801991e-17
---
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---
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---
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StyleLoss:updateOutput output 1: 17.913663864136
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StyleLoss:updateGradInput self.gradInput 2: -0.00054198916768655
dG 1: -2.9570073820651e-05
dG 2: -2.6757808208738e-12
---
Iteration 27 / 2500
Content 1 loss: 744754.687500
Style 1 loss: 50812.500000
Style 2 loss: 56938992.187500
Style 3 loss: 68948320.312500
Style 4 loss: 4786710.937500
Style 5 loss: 183966.110229
Total loss: 131653556.735229
optim value: -5.8849315643311
---
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gram updateOutput value: 2.1020674705505
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feval(x) grad value: 1.2366289773483e-17
---
StyleLoss:updateOutput input 1: 10.587713241577
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dG 2: -2.6954818585501e-12
---
Iteration 28 / 2500
Content 1 loss: 730892.919922
Style 1 loss: 51829.319000
Style 2 loss: 57855603.515625
Style 3 loss: 70133296.875000
Style 4 loss: 4849341.064453
Style 5 loss: 186422.138214
Total loss: 133807385.832214
optim value: -5.8498849868774
---
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gram updateOutput value: 2.0853116512299
gram updateOutput value 2: 727271488
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feval(x) grad value: 8.1264188758345e-17
---
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StyleLoss:updateOutput output 1: 17.1764087677
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dG 1: -2.9993638236192e-05
dG 2: -2.7141091023947e-12
---
Iteration 29 / 2500
Content 1 loss: 717856.591797
Style 1 loss: 52817.350388
Style 2 loss: 58730876.953125
Style 3 loss: 71282320.312500
Style 4 loss: 4909262.695312
Style 5 loss: 188752.681732
Total loss: 135881886.584854
optim value: -5.8159685134888
---
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gram updateOutput value: 2.0693266391754
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gram 3 value: -4.501567119064e-09
feval(x) grad value: 5.2998384152655e-18
---
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dG 2: -2.5320884278202e-11
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StyleLoss:updateOutput output 1: 56.272388458252
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dG 2: -2.9206053808045e-12
---
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StyleLoss:updateOutput output 1: 16.82758140564
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StyleLoss:updateGradInput self.gradInput 2: -0.00054199964506552
dG 1: -3.0187689844752e-05
dG 2: -2.7316686239398e-12
---
Iteration 30 / 2500
Content 1 loss: 705596.435547
Style 1 loss: 53774.208069
Style 2 loss: 59567267.578125
Style 3 loss: 72396398.437500
Style 4 loss: 4966305.175781
Style 5 loss: 190952.590942
Total loss: 137880294.425964
optim value: -5.7831435203552
---
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gram updateOutput value: 2.0540664196014
gram updateOutput value 2: 716777600
gram updateOutput value: 46.966171264648
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gram updateOutput value: 6.802948474884
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feval(x) grad value: 3.1799032973135e-17
---
StyleLoss:updateOutput input 1: 10.455035209656
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dG 2: -2.561831389386e-12
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dG 2: -5.5257989156621e-11
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dG 2: -2.5634844941225e-11
---
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dG 2: -2.9477570558101e-12
---
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StyleLoss:updateOutput output 1: 16.492895126343
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StyleLoss:updateGradInput self.gradInput 2: -0.00054200348677114
dG 1: -3.0370114473044e-05
dG 2: -2.7481758188563e-12
---
Iteration 31 / 2500
Content 1 loss: 694072.216797
Style 1 loss: 54705.951691
Style 2 loss: 60366638.671875
Style 3 loss: 73475789.062500
Style 4 loss: 5020632.934570
Style 5 loss: 193026.901245
Total loss: 139804865.738678
optim value: -5.7513709068298
---
x1 value: -5.7513709068298
gram updateOutput value: 2.0394680500031
gram updateOutput value 2: 711871232
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feval(x) grad value: 1.766612942952e-17
---
StyleLoss:updateOutput input 1: 10.414093017578
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dG 2: -2.5934143721429e-11
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dG 2: -2.7637419262871e-12
---
Iteration 32 / 2500
Content 1 loss: 683221.728516
Style 1 loss: 55609.434128
Style 2 loss: 61130373.046875
Style 3 loss: 74520035.156250
Style 4 loss: 5072547.729492
Style 5 loss: 194984.344482
Total loss: 141656771.439743
optim value: -5.7206163406372
---
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gram updateOutput value: 2.0255079269409
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feval(x) grad value: -2.0316047189586e-17
---
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dG 1: -3.0704428354511e-05
dG 2: -2.7784280952348e-12
---
Iteration 33 / 2500
Content 1 loss: 673064.550781
Style 1 loss: 56487.728119
Style 2 loss: 61860421.875000
Style 3 loss: 75530701.171875
Style 4 loss: 5122387.573242
Style 5 loss: 196831.741333
Total loss: 143439894.640350
optim value: -5.6908445358276
---
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gram updateOutput value: 2.0121378898621
gram updateOutput value 2: 702669056
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gram 1 value: -2.7423915582858e-08
gram 2 value: -5.4847831165716e-08
gram 3 value: -5.4847831165716e-08
gram 1 value: -7.3117369936426e-08
gram 2 value: -1.4623473987285e-07
gram 3 value: -1.4623473987285e-07
gram 1 value: -6.1503448023359e-07
gram 2 value: -1.2300689604672e-06
gram 3 value: -1.2300689604672e-06
gram 1 value: -5.4606067578788e-07
gram 2 value: -1.0921213515758e-06
gram 3 value: -1.0921213515758e-06
gram 1 value: -2.310716951115e-09
gram 2 value: -4.6214339022299e-09
gram 3 value: -4.6214339022299e-09
feval(x) grad value: 3.5332258859039e-18
---
StyleLoss:updateOutput input 1: 10.336686134338
StyleLoss:updateOutput output 1: 10.336686134338
StyleLoss:updateGradInput input 1: 10.336686134338
StyleLoss:updateOutput self.G 1: 702669056
StyleLoss:updateOutput self.G 2: 1.999324798584
StyleLoss:updateGradInput self.gradInput 1: -2.8452478151308e-09
StyleLoss:updateGradInput self.gradInput 2: -1.7071486581699e-05
dG 1: -0.0009199651540257
dG 2: -2.6176036165004e-12
---
StyleLoss:updateOutput input 1: 70.496421813965
StyleLoss:updateOutput output 1: 70.496421813965
StyleLoss:updateGradInput input 1: 70.496421813965
StyleLoss:updateOutput self.G 1: 7882967040
StyleLoss:updateOutput self.G 2: 44.859264373779
StyleLoss:updateGradInput self.gradInput 1: -5.6456457286913e-09
StyleLoss:updateGradInput self.gradInput 2: -3.8053953176131e-05
dG 1: -0.0098981717601418
dG 2: -5.63271165821e-11
---
StyleLoss:updateOutput input 1: 102.08964538574
StyleLoss:updateOutput output 1: 102.08964538574
StyleLoss:updateGradInput input 1: 102.08964538574
StyleLoss:updateOutput self.G 1: 4034073344
StyleLoss:updateOutput self.G 2: 45.872436523438
StyleLoss:updateGradInput self.gradInput 1: -1.0270849060134e-08
StyleLoss:updateGradInput self.gradInput 2: -6.3825405959506e-05
dG 1: -0.0023297960869968
dG 2: -2.649268304733e-11
---
StyleLoss:updateOutput input 1: 53.74690246582
StyleLoss:updateOutput output 1: 53.74690246582
StyleLoss:updateGradInput input 1: 53.74690246582
StyleLoss:updateOutput self.G 1: 273772448
StyleLoss:updateOutput self.G 2: 6.2152662277222
StyleLoss:updateGradInput self.gradInput 1: -1.9212977875327e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00012179723125882
dG 1: -0.00013313161616679
dG 2: -3.0223946175661e-12
---
StyleLoss:updateOutput input 1: 15.565598487854
StyleLoss:updateOutput output 1: 15.565598487854
StyleLoss:updateGradInput input 1: 15.565598487854
StyleLoss:updateOutput self.G 1: 5863013
StyleLoss:updateOutput self.G 2: 0.5305410027504
StyleLoss:updateGradInput self.gradInput 1: -9.033697523364e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00054202199680731
dG 1: -3.0857165256748e-05
dG 2: -2.7922495045296e-12
---
Iteration 34 / 2500
Content 1 loss: 663540.429688
Style 1 loss: 57341.972351
Style 2 loss: 62558542.968750
Style 3 loss: 76507746.093750
Style 4 loss: 5170224.609375
Style 5 loss: 198570.968628
Total loss: 145155967.042542
optim value: -5.6620225906372
---
x1 value: -5.6620225906372
gram updateOutput value: 1.999324798584
gram updateOutput value 2: 698348992
gram updateOutput value: 44.859264373779
gram updateOutput value 2: 7801437184
gram updateOutput value: 45.872436523438
gram updateOutput value 2: 3967986944
gram updateOutput value: 6.2152662277222
gram updateOutput value 2: 267965600
gram updateOutput value: 0.5305410027504
gram updateOutput value 2: 5654413
gram 1 value: -2.7013399517273e-08
gram 2 value: -5.4026799034546e-08
gram 3 value: -5.4026799034546e-08
gram 1 value: -7.2659332772673e-08
gram 2 value: -1.4531866554535e-07
gram 3 value: -1.4531866554535e-07
gram 1 value: -6.1631772041437e-07
gram 2 value: -1.2326354408287e-06
gram 3 value: -1.2326354408287e-06
gram 1 value: -5.4593249387835e-07
gram 2 value: -1.0918649877567e-06
gram 3 value: -1.0918649877567e-06
gram 1 value: -2.3241661928353e-09
gram 2 value: -4.6483323856705e-09
gram 3 value: -4.6483323856705e-09
feval(x) grad value: 7.9497575815393e-17
---
StyleLoss:updateOutput input 1: 10.300073623657
StyleLoss:updateOutput output 1: 10.300073623657
StyleLoss:updateGradInput input 1: 10.300073623657
StyleLoss:updateOutput self.G 1: 698348992
StyleLoss:updateOutput self.G 2: 1.9870326519012
StyleLoss:updateGradInput self.gradInput 1: -2.8452744604834e-09
StyleLoss:updateGradInput self.gradInput 2: -1.7071646652767e-05
dG 1: -0.00092596706235781
dG 2: -2.6346808849192e-12
---
StyleLoss:updateOutput input 1: 70.096145629883
StyleLoss:updateOutput output 1: 70.096145629883
StyleLoss:updateGradInput input 1: 70.096145629883
StyleLoss:updateOutput self.G 1: 7801437184
StyleLoss:updateOutput self.G 2: 44.395313262939
StyleLoss:updateGradInput self.gradInput 1: -5.6458939745596e-09
StyleLoss:updateGradInput self.gradInput 2: -3.7658664950868e-05
dG 1: -0.0099548073485494
dG 2: -5.6649400448361e-11
---
StyleLoss:updateOutput input 1: 101.24900054932
StyleLoss:updateOutput output 1: 101.24900054932
StyleLoss:updateGradInput input 1: 101.24900054932
StyleLoss:updateOutput self.G 1: 3967986944
StyleLoss:updateOutput self.G 2: 45.120952606201
StyleLoss:updateGradInput self.gradInput 1: -1.0295768682056e-08
StyleLoss:updateGradInput self.gradInput 2: -6.3818828493822e-05
dG 1: -0.0023527294397354
dG 2: -2.675346229275e-11
---
StyleLoss:updateOutput input 1: 53.164566040039
StyleLoss:updateOutput output 1: 53.164566040039
StyleLoss:updateGradInput input 1: 53.164566040039
StyleLoss:updateOutput self.G 1: 267965600
StyleLoss:updateOutput self.G 2: 6.0834360122681
StyleLoss:updateGradInput self.gradInput 1: -1.9223996616802e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00012178126053186
dG 1: -0.00013413738633972
dG 2: -3.0452287826804e-12
---
StyleLoss:updateOutput input 1: 15.280905723572
StyleLoss:updateOutput output 1: 15.280905723572
StyleLoss:updateGradInput input 1: 15.280905723572
StyleLoss:updateOutput self.G 1: 5654413
StyleLoss:updateOutput self.G 2: 0.51166492700577
StyleLoss:updateGradInput self.gradInput 1: -9.0337941571761e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00054202770115808
dG 1: -3.1001181923784e-05
dG 2: -2.8052807472811e-12
---
Iteration 35 / 2500
Content 1 loss: 654587.890625
Style 1 loss: 58173.219681
Style 2 loss: 63226347.656250
Style 3 loss: 77451996.093750
Style 4 loss: 5216204.223633
Style 5 loss: 200212.188721
Total loss: 146807521.272659
optim value: -5.6341166496277
---
x1 value: -5.6341166496277
gram updateOutput value: 1.9870326519012
gram updateOutput value 2: 694198784
gram updateOutput value: 44.395313262939
gram updateOutput value 2: 7723768832
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