eridgd / adain-tf Goto Github PK

Hi, do you have a decoder model you've already trained that I could test?

Hi, I was wondering why you have calculated sum of squared in style loss calculations:

m_loss = sse(d_mean, s_mean) / batch_size # normalized w.r.t. batch size

While in torch implementation MSE is being used, this line:

self.mean_criterion = nn.MSECriterion() self.mean_loss = self.mean_criterion:forward(self.input_mean, self.target_mean)

Hi, just wondering if it's possible to view the visual transfers as it progresses through training?
Not sure how this is done in tensorflow but in the original torch implementation you can view the training images . Any ideas?
Cheers

Hi Eridgd,

I saw most of the AdaIN-TF implementation used the normalised-vgg19 network as the encoder (include the original author)

Do you know the difference between normalised_vgg network and original VGG-19 network? And can we implement AdaIN-TF with the original vgg-19 network as encoder ?

Hey dude, just getting some errors when trying to train...any ideas?
(tf-keras) E:\SUGARBANK\ML\SOFTWARE\AdaIN-TF-master>python train.py --content-path E:\SUGARBANK\ML\SOFTWARE\train2014 --style-path E:\SUGARBANK\ML\ASSETS\Style --batch-size 8 --content-weight 1 --style-weight 1e-2 --tv-weight 0 --checkpoint E:\SUGARBANK\ML\AdaIN-TF-master\models\checkpoint --learning-rate 1e-4 --lr-decay 1e-5
Using TensorFlow backend.
2018-02-06 12:30:31.142079: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2018-02-06 12:30:32.247307: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 980M major: 5 minor: 2 memoryClockRate(GHz): 1.1265
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.71GiB
2018-02-06 12:30:32.248644: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 980M, pci bus id: 0000:01:00.0, compute capability: 5.2)
Traceback (most recent call last):
File "train.py", line 189, in
train()
File "train.py", line 128, in train
use_gram=args.gram)
File "E:\SUGARBANK\ML\SOFTWARE\AdaIN-TF-master\model.py", line 18, in init
self.build_model(vgg_weights)
File "E:\SUGARBANK\ML\SOFTWARE\AdaIN-TF-master\model.py", line 31, in build_model
self.vgg_model = vgg_from_t7(vgg_weights, target_layer='relu4_1')
File "E:\SUGARBANK\ML\SOFTWARE\AdaIN-TF-master\vgg_normalised.py", line 21, in vgg_from_t7
t7 = torchfile.load(t7_file, force_8bytes_long=False)
File "C:\Users\Sugar\AppData\Local\conda\conda\envs\tf-keras\lib\site-packages\torchfile.py", line 424, in load
return reader.read_obj()
File "C:\Users\Sugar\AppData\Local\conda\conda\envs\tf-keras\lib\site-packages\torchfile.py", line 370, in read_obj
obj._obj = self.read_obj()
File "C:\Users\Sugar\AppData\Local\conda\conda\envs\tf-keras\lib\site-packages\torchfile.py", line 385, in read_obj
k = self.read_obj()
File "C:\Users\Sugar\AppData\Local\conda\conda\envs\tf-keras\lib\site-packages\torchfile.py", line 386, in read_obj
v = self.read_obj()
File "C:\Users\Sugar\AppData\Local\conda\conda\envs\tf-keras\lib\site-packages\torchfile.py", line 370, in read_obj
obj._obj = self.read_obj()
File "C:\Users\Sugar\AppData\Local\conda\conda\envs\tf-keras\lib\site-packages\torchfile.py", line 387, in read_obj
obj[k] = v
TypeError: unhashable type: 'list'

HI is there a CPU mode available? or is this GPU only?

Did you implement the Spatial Control? when I implement the Spatial control, I have some question。