I’m trying to implement a DCGAN, with heavy inspiration from DCGAN example from the Pytorch github 30 and a dataset of 64x64 images, but I’ve been unable to increase the filter sizes of the convolutional layers in the discriminator and generator. The implementation works with a max filter value of 512 (e.g. Conv2d(256,512,kernel_size, stride), but I have had no luck trying to increase the filter value to a max of 1024.
The above implementation’s discriminator is as follows (which I have used in my implementation):
nc = 3
ndf = 64
  self.main = nn.Sequential(
            # input is (nc) x 64 x 64
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf) x 32 x 32
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*2) x 16 x 16
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*4) x 8 x 8
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*8) x 4 x 4
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

I want to change the model such that the last layer is:
nn.Conv2d(ndf*16,1,4,1,0,bias=False)
so that the final layer has 1024 filters, as opposed to 512 (and similarly for the generator). However, when I implement this, changing only that and keeping the kernel size, channels (nc) etc. constant, I get
RuntimeError: Calculated input size: (2 x 2). Kernel size: (4 x 4). Kernel size can't greater than actual input size
How do I construct a discriminator with a final convolutional layer that has 1024 output filters (and the equivalent convolutional transpose layer in the generator with 1024 input filters)? I imagine I should be able to do this without changing the inputs (batch of 64 images that are 64px by 64px).
Any help would be greatly appreciated.