Issue Running CW Attack with PyTorch

[christymarc]

I am working with Pytorch 1.13.1 and Python 3.10.12.

When using the Cleverhans CW attack for Pytorch, the attack script runs into 3 errors.

1) On line 108 of the attack py file:

const = x.new_ones(len(x), 1) * initial_const

The following error comes up:

TypeError: new_ones(): argument 'dtype' must be torch.dtype, not int

To solve this, I assumed the 1 was supposed to denote the dimension of the tensor, rather than a dtype, so I wrapped the function call in another set of parenthesis:

const = x.new_ones((len(x), 1)) * initial_const

2) On line 134:

optimizer = torch.optim.Adam([modifier], lr=lr)

I get the following error:

ValueError: can’t optimize a non-leaf Tensor

This is due to line 123:

modifier = torch.zeros_like(x, requires_grad=True)

Which returns a non-leaf Tensor. I was thinking at some point maybe a version update changed this function to not return a leaf tensor, but regardless, I fixed it using Pytorch's zeros function, since the documentation of zeros_like described these functions as equivalent if you give them different parameters accordingly. The edited line looks like this:

modifier = torch.zeros(x.size(), requires_grad=True, dtype=x.dtype, layout=x.layout, device=x.device)

3. On line 155:

   loss.backward()

   I received this error:

   RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.

   To solve this, I just specified retain_graph=True:

   loss.backward(retain_graph=True)

   But I'm not sure it this is the most efficient fix...

While the solutions I implemented make the attack script run and seemingly work, I am not sure if maybe I am missing something or if I have unknowingly changed the code's functionality in some way. So I would really appreciate any feedback and/or guidance.