Do this:
class Net(nn.Module):
def __init__(self):
    super(Net, self).__init__()
    self.conv1 = nn.Conv2d(3, 6, 5)
    self.conv2 = nn.Conv2d(6, 16, 5)
    self.pool = nn.MaxPool2d(2, 2)
    self.fc1 = nn.Linear(16 * 5 * 5, 120)
    self.fc2 = nn.Linear(120, 84)
    self.fc3 = nn.Linear(84, 8)

def forward(self, x):
    x = self.pool(F.relu(self.conv1(x)))
    x = self.pool(F.relu(self.conv2(x)))
    #x = x.view(x.size(0), -1)
    #x = F.relu(self.fc1(x))
    #x = F.relu(self.fc2(x))
    #x = self.fc3(x)
    return x

CNNModel = Net()
random_input = torch.randn(32, 3, 100, 100) (where 100x100 is your image dimension)

out = CNNModel(random_input)
out.shape

your out shape would be something like [32, C, W, H] and this C * W * H should be given as input to your self.fc1.