regarding caffe to tensorflow

Question

Currently, there are a lot of deep learning models developed in Caffe instead of tensorflow. If I want to re-write these models in tensorflow, how to start? I am not familiar with Caffe structure. It seems to me that there are some files storing the model architecture only. My guess is that I only need to understand and transfer those architecture design into Tensorflow. The input/output/training will be re-written anyway. Is this thought meaningful?

I see some Caffe implementation also need to hack into the original Caffe framework down to the C++ level, and make some modifications. I am not sure under what kind of scenario the Caffe model developer need to go that deep? If I just want to re-implement their models in Tensorflow, do I need to go to check their C++ modifications, which are sometimes not documented at all.

I know there are some Caffe-Tensorflow transformation tool. But there are always some constraints, and I think re-write the model directly maybe more straightforward.

Any thougts, suggestions, and link to tutorials are highly appreciated.

Answer 1

I have already asked a similar question.

To synthetise the possible answers :

1. You can either use pre-existing tools like etheron's kaffe(which is really simple to use). But its simplicity comes at a cost: it is not easy to debug.

2. As @Yaroslav Bulatov answered start from scratch and try to make each layer match. In this regard I would advise you to look at ry's github which is a remarkable example where you basically have small helper functions which indicate how to reshape the weights appropriately from caffe to Tensorflow, which is the only real thing you have to do to make simple models match and also provides activations check layer by layer.