The above tutorial uses a pre-trained neural VGG network but does not adjust the images for mean or standard deviation. Since the Pytorch model zoo was trained using these transforms I would have thought that would reduce the accuracy of the model. I guess since its style transfer it might not matter but I was puzzled as to the reason this wasn’t done?
Any comments / suggestions
Thanks
John
Well the reason is simple: to make it simpler 
Actually you are right, VGG has been trained with preprocessed images, so it may be cleaner to do the same on the input images for neural transfert. I simply noticed that it was not improving the results while adding some lines of code in the tutorial.
If you want to try with preprocess correction you can do:
# pre and post processing for images
img_size = 512
prep = transforms.Compose([transforms.Scale(img_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x[torch.LongTensor([2,1,0])]), #turn to BGR
transforms.Normalize(mean=[0.40760392, 0.45795686, 0.48501961], #subtract imagenet mean
std=[1,1,1]),
transforms.Lambda(lambda x: x.mul_(255)),
])
postpa = transforms.Compose([transforms.Lambda(lambda x: x.mul_(1./255)),
transforms.Normalize(mean=[-0.40760392, -0.45795686, -0.48501961], #add imagenet mean
std=[1,1,1]),
transforms.Lambda(lambda x: x[torch.LongTensor([2,1,0])]), #turn to RGB
])
postpb = transforms.Compose([transforms.ToPILImage()])
def postp(tensor): # to clip results in the range [0,1]
t = postpa(tensor)
t[t>1] = 1
t[t<0] = 0
img = postpb(t)
return img
(source: https://github.com/leongatys/PytorchNeuralStyleTransfer/blob/master/NeuralStyleTransfer.ipynb)
Thanks very much, thought that was likely to be the case but just wanted to check. I notice that you have set the std dev to zero wheras in the pytorch model zoo documentation is is set to std = [0.229, 0.224, 0.225]. I guess that using this will make the clipping problem worse such that the target image will then lose significant information.
Leon Gatys is using his VGG19 Conv model that was converted from Caffe to PyTorch. So the pre-processing is going to be different for that model, than the model zoo models which were trained in PyTorch. So the code that @alexis-jacq linked to is not correct for the model which his style transfer tutorial uses.
Thanks, I’m getting some issues when I use preprocessing of the sort recommended for the Pytorch model zoo, will explore further tomorrow as it’s late in UK now