matrix multiplication instead of scalar dot product

[vainaixr]

I think they use matrix multiplication, not dot product

[vainaixr]

therefore, I think, final lines should be like,

        k_out = k_out.contiguous().view(batch, self.groups, height, width, -1, self.out_channels // self.groups)
        p_out = p_out.contiguous().view(batch, self.groups, height, width, -1, self.out_channels // self.groups)
        v_out = v_out.contiguous().view(batch, self.groups, height, width, -1, self.out_channels // self.groups)

        q_out = q_out.view(batch, self.groups, height, width, 1, self.out_channels // self.groups)

        out = torch.matmul(q_out, (k_out).transpose(-1, -2))
        out = F.softmax(out, dim=-1)

        out = torch.matmul(out, v_out).view(batch, -1, height, width)
        return out

[KinWaiCheuk]

After studying the code line by line, I also have this doubt.
I was wondering if the element-wise product used in this code is another type of attention mechanism.

But at least in the paper equation (2) is of the form

which can be achieved by the code proposed by @vainaijr

[KinWaiCheuk]

I realized that this issue is the duplicate of #10

[MartinGer]

Are there any updates to this issue? Even though this repo has now over 300 stars, the implementation is different from what is described in the paper or other implementations as https://arxiv.org/pdf/1904.09925.pdf.
For example, the number of groups/heads in this implementation doesn't seem to make any difference.
What confuses me is that while testing I could achieve better results with this implementation than with the ones described in the papers. I couldn't find any explanation though.