I am training a LNN with pytorch 1.1.0 and using the training to perform an inference on C++.
Due to various compatibility issues with CERN ROOT (long story) I cannot use the C++ torch libraries, so I re-implemented the entire forward process in C++ using matrix algebra.
In order to do so, I wrote a python script to convert the training .pt file in three separate csv files (weights, biases, norm) to be fed to the C++ script. This works fine, but is obviously very annoying.
I want to directly load and deserialize the pytorch .pt model in C++ without using any torch-related library. I had a look at the source code for the load() function and it looks it is based on the pickle library, but I failed in loading the .pt file using pickle.
Could anyone help me understand how the deserialization process is performed in pytorch?
The root of the annoyance is not using libtorch.
I’m assuming here you want state dicts (rather than, say, JIT models).
But so PyTorch’s save function is implemented in torch/serialization.py and for storage (Tensor doesn’t store it’s own memory, but delegates to a TensorStorage), these will be deserialized by the code in torch/csrc/generic/serialization.cpp.
Maybe the quickest way is is to look beyond pure PyTorch and store in some more common format, e.g. form numpy (don’t know if there is good C++ support for those) or hdf5.
Storing info using another format is more or less my current solution, but I wanted to avoid having two copies of each training file.
I’ll have a look at the code you posted, maybe I can come up with a solution.
