Questions regarding translating Lua code to PyToch

1

This is the whole PyTorch script: https://gist.github.com/ProGamerGov/753b64404547662b9ff3d816a7f88f9f#file-test2-py-L144-L195

Specifically I am having trouble trying to translate these functions that were made for Lua’s Torch7 library, into Python’s PyTorch Library: https://github.com/jcjohnson/neural-style/blob/master/neural_style.lua#L449-L564

For example, the Lua code uses:

self.gradInput:div(torch.norm(self.gradInput, 1) + 1e-8)

And I translated it into:

self.gradInput.div_(torch.norm(self.gradInput, 1) + 1e-8) # Normalize Gradients

But I can’t get any form of it to work inside the ContentLoss or StyleLoss function with the input variable.

Trying to implement any of the other parts of the Lua ContentLoss and StyleLoss functions, also seems to result in the same errors:

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

I am using the nn.Module for the ContentLoss and StyleLoss functions.

2

Not really familiar with Lua Torch, but the error seems to be coming from using inplace operations like .div_ on Variables. You could try replacing those with their counterparts, like .div and checking if it fixes your code.
From the docs:

Supporting in-place operations in autograd is a hard matter, and we discourage their use in most cases.

You can read more about it here: Autograd mechanics

3

Does one have to use autograd? Or are there other options in PyTorch?

4

Is there a different “resizeAs” in PyTorch?

AttributeError: 'Variable' object has no attribute 'resizeAs_'

The error comes from this code:

class ContentLoss(nn.Module):

    def __init__(self, target, strength):
        super(ContentLoss, self).__init__()
        self.target = target.detach() * strength
        self.strength = strength
        self.crit = nn.MSELoss()
        self.register_parameter('loss_mode', None)
	    self.normalize = 'Yes'
		
		
    def forward(self, input):
      #self.output = input.clone()
	  if self.loss_mode is None:
	    self.target.resizeAs_(input).clone(input)
	  else: 
	    self.loss = self.crit(self.G, self.target) * self.strength       
	  self.output = input 
      return self.output

    def backward(self, input, gradOutput):
	  if self.loss_mode is None:
        self.gradInput.resizeAs_(gradOutput).clone(gradOutput)

	  else:
	    if input.data.nelement() == self.target.nelement():
	      self.gradInput = self.crit(input, self.target)
	    if self.normalize == 'Yes':
	      self.gradInput.div_(torch.norm(self.gradInput, 1) + 1e-8) # Normalize Gradients
	    self.gradInput.mul_(self.strength)
        self.gradInput.add_(gradOutput)

		  
        #self.loss.backward(retain_graph=retain_graph)
      return self.gradInput
5

You have a typo. Try Variable.resize_as_.