Hello, is there any efficient way to determine the size of the output of an intermediate layer considering the input size ? For an instance, in the following code snippet the output of conv_3 has the size of N x 256 x 8 x 8 for for an input of size N x 1 x 64 x 64 . So the in_features of fc1 will be 256 x 8 x 8 . Instead of manually calculating this value for any particular input dimension, is there any way to automate this? Thank you for any help.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 64, 3, padding=1)
self.conv1_ = nn.Conv2d(64, 64, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
self.conv2_ = nn.Conv2d(128, 128, 3, padding=1)
self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
self.conv3_ = nn.Conv2d(256, 256, 3, padding=1)
self.flat = nn.Flatten()
self.fc1 = nn.Linear(256 * 8 * 8, 15)
def forward(self, x):
x = (F.leaky_relu(self.conv1(x)))
x = self.pool(F.leaky_relu(self.conv1_(x)))
x = (F.leaky_relu(self.conv2(x)))
x = self.pool(F.leaky_relu(self.conv2_(x)))
x = (F.leaky_relu(self.conv3(x)))
x = self.pool(F.leaky_relu(self.conv3_(x)))
x = self.flat(x)
x = (self.fc1(x))
return x