Hi,
I’m trying to walk through by hand the size of input at each layer of my CNN network.
class Model(nn.Module):
  def __init__(self):
    super(Model, self).__init__()
    self.conv1 = nn.Conv2d(1, 10, 3)
    self.pool1 = nn.MaxPool2d(2)
    self.conv2 = nn.Conv2d(10, 20, 3)
    self.pool2 = nn.MaxPool2d(2)
    self.conv3 = nn.Conv2d(20, 32, 3)
    self.pool3 = nn.MaxPool2d(2)
    self.linear1 = nn.Linear(32, 10)
  
  def forward(self, x):
    x = self.conv1(x)
    x = self.pool1(x)
    x = F.relu(x)
    x = self.conv2(x)
    x = self.pool3(x)
    x = F.relu(x)
    x = self.conv3(x)
    x = self.pool3(x)
    x = F.relu(x)
    print(x.shape)
    x = self.linear1(x)
    return F.softmax(x)

I’m using the MNIST dataset as input
Input size: 28x28
Size After conv1 layer: 26x26
Size After Pool1 layer: 13x13
Size After conv2 layer: 11x11
Size After Pool2 layer: 5x5
Size After conv3 layer: 3x3
Size After Pool3 layer: 1x1
So number of channels at input of linear is 32x1x1=32
But I get an error
from torch.autograd import Variable
maxPoolLayer = nn.MaxPool2d(2)
conv2dLayer = nn.Conv2d(1, 10, 3)
mA = Variable(torch.randn(1, 1, 25, 25))
mA.size()
mC = conv2dLayer(mA)
print(mC.size())
mB = maxPoolLayer(mC)
print(mB.size())

Output:
torch.Size([1, 10, 23, 23])
torch.Size([1, 10, 11, 11])
So default conv2d layer will reduce the size on input by 2 for both dimensions and maxpooling will floor-half the input