In v0.4, is there any reason to still use

For example, here is a snippet of code of old version of pytorch:

# classifier is the classifier of a torchvision pre-trained model
fc6 = nn.Conv2d(512, 4096, kernel_size=7)[0], 512, 7, 7))[0]

Is there a better way to write this in version 0.4?

Thanks in advance!

You simply need to remove the .data

operations on .data are hidden from autograd. in this case, if you used weight and bias in a graph before this segment, and don’t do .data or .detach or with torch.no_grad(), autograd may complain about necessary tensors being modified inplace.

As I understood migration guide correctly, we can just simply replace code with .data to .detach if operation was not inplace and torch.no_grad() if it was inplace operation. Like in this example: Detach and .data

Yes you can generally do that, unless you are doing some hacks that you want hidden from autograd :wink:

I have a layer that is forward-only and using .data was the simplest way to implement is.

    second_loss(func(, y)

I could use no_grad context, but that would have been not as elegant because I do want grad for second_loss, just not for func.

why not to use x.detach() ?

What happens if I have second_loss(func(x.detach()), func2(x.detach()), y), then it would only matter if x.detach(), being a function that I do not know its implementation, has an overhead or not.

Given that is a property that does function calls in the background to re-wrap the underlying stuff in a new variable, I would not know the overhead of either (but believe both are similar, after looking at the implementation).

The migration guide fairly clearly advises

Best regards


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