This repository contains tools to generate and consume NNEF documents, such as a parser (C++ and Python) that can be included in consumer applications and converters for deep learning frameworks.

Much of the tools have been recently refactored into a new nnef_tools package. This is an ongoing work, and some of the converters (Caffe, Caffe2) have not yet been migrated to this new package. These converters can be found in the 'legacy' folder.

A Model Zoo is now available; the 'models' folder contains a variety of NNEF models converted from various sources.

According to a change in version 1.0.1 of the NNEF specification, the shape_of operator in NNEF syntax is deprecated, and the parser does not support it. This enables the decoupling of parsing from shape inference, allowing parsing to succeed even if shape information is not available for all operations, such as custom defined operations before the graph definition. Shape inference can still be run after training, furthermore it can be customized (via function pointers) for custom defined operations.

We have recently found a bug in the Python code that reads/writes the tensor binary files (the header contained 4 extra padding bytes therefore not conforming to the spec). We have updated the code to read/write and check the proper header size. As a consequence, any files written out with the code that contained the bug cannot be read back with the updated code. To aid the usage of such existing files, we have created a script called fix_nnef_binary_size.py that can be used to remove the excess 4 bytes from existing NNEF files. The script is located in the root folder of this repo, it has no dependencies (not even the NNEF parser). It can be run on the main folder of an NNEF model, and it fixes all binary files in the folder. In case one runs it on an NNEF model that does not contain the bug, it does nothing. It can be used as follows:

python fix_nnef_binary_size.py my_nnef_model_folder

Such an invocation fixes the files in place. Optionally, a second argument can be supplied to the script to write the fixed files to a different output path. In this case, the script copies all non-binary files (such as graph.nnef) to the target folder, so the resulting folder contains the whole valid model.