How to construct a network with two inputs in PyTorch
Suppose I want to have the general neural network architecture:

Input1 --&gt; CNNLayer 
                    \
                     ---&gt; FCLayer ---&gt; Output
                    /
Input2 --&gt; FCLayer


Input1 is image data, input2 is non-image data. I have implemented this architecture in Tensorflow.

All pytorch examples I have found are one input go through each layer. How can I define forward func to process 2 inputs separately then combine them in a middle layer? 

By "combine them" I assume you mean to concatenate the two inputs.
Assuming you concat along the second dimension:

import torch
from torch import nn

class TwoInputsNet(nn.Module):
  def __init__(self):
    super(TwoInputsNet, self).__init__()
    self.conv = nn.Conv2d( ... )  # set up your layer here
    self.fc1 = nn.Linear( ... )  # set up first FC layer
    self.fc2 = nn.Linear( ... )  # set up the other FC layer

  def forward(self, input1, input2):
    c = self.conv(input1)
    f = self.fc1(input2)
    # now we can reshape `c` and `f` to 2D and concat them
    combined = torch.cat((c.view(c.size(0), -1),
                          f.view(f.size(0), -1)), dim=1)
    out = self.fc2(combined)
    return out


Note that when you define the number of inputs to self.fc2 you need to take into account both out_channels of self.conv as well as the output spatial dimensions of c.
