I’m trying to understand the lines below:
class MyModule(nn.Module):
    def __init__(self, in_channels, out channels):
        super(MyModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv3_3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=3, dilation=3)
    def forward(self, x):
        x_y = self.avg_pool(x)
        x_c = self.conv3_3(x_y)
        return x_c

which translates into: CONV3_DIL3(AVG_POOL1(X)). Now:

Imagine that X.shape = [1, 64, 16, 16], i.e. BxCxHxW.
After applying the  X_Y=AVG_POOL1(X), we obtain X_Y.shape = [1, 64, 1, 1] => 1xCx1x1

Finally, after applying X_C= CONV3_DIL3(X_Y) we obtain X_Y.shape = [1, 64, 1, 1] => 1xCx1x1 because of the padding.

Now, my questions are: (i) what does it exactly mean to apply a 2D Conv with a 3x3 filter with dilation=3 into a 1xCx1x1 tensor, especially because there’s no spatial information in a flat 1xCx1x1 tensor (ii) why does this make sense at all?