Why use nn.Conv2D over nn.functional.conv2d?

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Hi, I stumbled upon this question because I am trying to have control over how my convolutional weights are initialized.

At any rate, we can create a 2D convolutional layer via nn.functional.conv2d, or, via nn.Conv2d

The API for both of those however seems different.

For the former:

torch.nn.functional.conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1)

For the latter:

class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)

So, my questions are the following:

  1. Why should I use one over the other?
  2. I am trying to initialize my convolutional weights with what I want, so is that possible using nn.Conv2D? I guess one way is to copy over the proper weights that I want, so is that basically it?

Thanks

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  1. It is recommended to use nn.Conv2D because it uses the nn.Module abstraction and nicely ties into the torch.optim framework well.

  2. Yes, here’s an example of initializing the weights of a ConvNet via a custom weight initialization:
    https://github.com/pytorch/examples/blob/c6bff8c51eee802be1a77575be0eb3eb5e211ada/dcgan/main.py#L89-L96,L131
    https://github.com/pytorch/examples/blob/c6bff8c51eee802be1a77575be0eb3eb5e211ada/dcgan/main.py#L131

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