`*** RuntimeError: mat1 dim 1 must match mat2 dim 0` whenever I run model(images)
    def __init__(self):
        super().__init__()

        self.conv = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=2, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),

            nn.Conv2d(64, 64, kernel_size=3, stride=2, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            
            nn.Conv2d(64, 64, kernel_size=3, stride=2, bias=False),
            nn.BatchNorm2d(64),
  
        )

How can I deal with this error? I think the error is with self.fc, but I can't say how to fix it.

The output from self.conv(x) is of shape torch.Size([32, 64, 2, 2]): 32*64*2*2= 8192 (this is equivalent to (self.conv_out_size). The input to fully connected layer expects a single dimension vector i.e. you need to flatten it before passing to a fully connected layer in the forward function.
i.e.
class Network():
    ...
    def foward():
    ...
        conv_out = self.conv(x)
        print(conv_out.shape)
        conv_out = conv_out.view(-1, 32*64*2*2)
        print(conv_out.shape)
        x = self.fc(conv_out)
        return x

output
torch.Size([32, 64, 2, 2])
torch.Size([1, 8192])


EDIT:
I think you're using self._get_conv_out function wrong.
It should be
    def _get_conv_out(self, shape):
        output = self.conv(torch.zeros(1, *shape)) # not (32, *size)
        return int(numpy.prod(output.size()))

then, in the forward pass, you can use
        conv_out = self.conv(x)
        # flatten the output of conv layers
        conv_out = conv_out.view(conv_out.size(0), -1)
        x = self.fc(conv_out)

For an input of (32, 1, 110, 110), the output should be torch.Size([32, 2]).
