Yes, according to the previous code, the source is from image2

  
      github.com
  
  
    NVlabs/PWC-Net/blob/master/PyTorch/models/PWCNet.py#L139 117

    self.dc_conv6 = conv(64,       32,  kernel_size=3, stride=1, padding=1,  dilation=1)
    self.dc_conv7 = predict_flow(32)


    for m in self.modules():
        if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
            nn.init.kaiming_normal(m.weight.data, mode='fan_in')
            if m.bias is not None:
                m.bias.data.zero_()




def warp(self, x, flo):
    """
    warp an image/tensor (im2) back to im1, according to the optical flow


    x: [B, C, H, W] (im2)
    flo: [B, 2, H, W] flow


    """
    B, C, H, W = x.size()
    # mesh grid 
    xx = torch.arange(0, W).view(1,-1).repeat(H,1)