I’m not using GPUs. I think i need to re-size my image when using the VGG model as below. If i do so, i get the following error message. Are you able to advise what i need to change please?
RuntimeError: Calculated padded input size per channel: (1 x 1). Kernel size: (3 x 3). Kernel size can't be greater than actual input size

The code for my convnet file:
import torch
import torch.nn as nn

class Convnet(nn.Module):
    """
    A custom convnet for convolving 1080x1080 fluorescence micrograph images.
    """

    def __init__(self):
        super(Convnet,self).__init__()
        self.main = nn.Sequential(

            nn.Conv2d(3,16,10,stride=2,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(16,32,7,stride=2,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(32,64,3,stride=3,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(64,96,3,stride=2,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(96,128,3,stride=2,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(128,192,3,stride=2,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(192,256,3,stride=2,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(256,256,3,stride=2,padding=0),
            nn.LeakyReLU(0.2,inplace=False),
            nn.Conv2d(256,1,1,stride=1,padding=0)
        )

    def forward(self, x):
        # x: B x 2 x 1024 x 1024
        return self.main(x) # B x 1 x 9 x 9

Thanks a lot