ProGamerGov

I finally have a working solution which doesn't try to anything on the backwards pass. It follows PyTorch's strengths, and I have gotten many of `neural_style.lua`'s features working in it:...

So previously, I wasn't doing any sort of pre-processing like converting to and from BGR or subtracting/adding the mean pixel values. I also wasn't rescaling from [0, 1] to [0,...

> So it is actually a wonder you got so decent results earlier. The model needs proper value range and channel order to respond as expected. Yea, I'm surprised that...

This line results in: ``` self.loss.div_(self.loss.norm(0) + 1e-8) ``` ``` RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation ``` I think the...

For the content/style weight issue, it seems as though there is some sort of limit that scales higher weights to match the maximum allowed weight value. I've messed around with...

I should also add that for the first iteration, the content loss is normally zero, and then for the next 1-2 iterations, it's an extremely low value. Then after that,...

This is the result of `-init random`. Different content and style weights have the same issue with `-init random`. I think it may have been working a bit differently before,...

> I had in mind, for instance, how the pytorch neural-style tutorial clamps the values between 0 and 1 to keep the pixel values in range, if something like that...

@htoyryla I haven't seen a pickle error in relation to the script before. I've been using the pip version of PyTorch via `pip`: ``` pip install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp27-cp27mu-linux_x86_64.whl pip install torchvision...

> BTW, it is not obvious what your curves mean. You say it is the image value, but an image is a 3D tensor, so what does the curve show....