I expect that most people are using ONNX to transfer trained models from Pytorch to Caffe2 because they want to deploy their model as part of a C/C++ project.  However, there are no examples which show how to do this from beginning to end.
From the Pytorch documentation here 112, I understand how to convert a Pytorch model to ONNX format using torch.onnx.export, and also how to load that file into Caffe2 using onnx.load  + onnx_caffe2.backend… but  that’s just the python side of things.
I also understand something about loading pretrained Caffe2 models into a C++ project from .pb files as described in the Caffe2 C++ tutorials here 281.  (Click on the pretrained.cc 134 link.)
What I’m missing are the steps in between.  How do I take the output from onnx_caffe2.backend and create the .pb files found in the Caffe2 C++ tutorials?  Maybe these steps are obvious to a seasoned Caffe2 user, but this is my first exposure.  A step-by-step recipe would probably help a lot of Pytorch users.