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NNet Converter

The following Python scripts are used to convert between different neural network file formats. The supported file formats are as follows:

  • .onnx (Open Neural Network Exchange): Specification that defines how models should be constructed and the operators in the graph. Open-source project under the Linux Foundation.
  • .pb (protobug): Used by TensorFlow's serving when the model needs to be deployed for production. Open-source project that is currently overviewd by Google.
  • .h5 (HDF5 binary data format): Originally used by Keras to save models. This file format is less general and more "data-oriented" and less programmatic than .pb, but simpler to use than .pb. It is easily convertible to .pb.
  • .nnet (NNet): Developed by the Stanford Intelligent Systems Laboratory, initially to define aircraft collision avoidance neural networks in human-readable text document. This format is a simple text-based format for feed-forward, fully-connected, ReLU-activate neural networks.
  • .pt (PyTorch): Used by PyTorch.

H5 to ONNX

Converts a .h5 model to an .onnx model.

~/ModelVerification.jl/NNet/converters$ python h52onnx.py --model_path "[path/to/h5/file]" --name_model "[path/to/converted/onnx/file]" --test_conversion [True/False]

NNET to ONNX

Converts a .nnet model to an .onnx model.

~/ModelVerification.jl/NNet/converters$ python nnet2onnx.py [nnetFile] [onnxFile] [outputName] [normalizeNetwork]

where

  • nnetFile: (string) .nnet file to convert to onnx.
  • onnxFile: (string, optional) Optional, name for the created .onnx file.
  • outputName: (string, optional) Optional, name of the output variable in onnx.
  • normalizeNetwork: (bool, optional) If true, adapt the network weights and biases so that networks and inputs do not need to be normalized. Default is False.

NNET to PB

Converts a .nnet model to a .pb model.

~/ModelVerification.jl/NNet/converters$ python nnet2pb.py [nnetFile] [pbFile] [output_node_names]
  • nnetFile (string): A .nnet file to convert to Tensorflow format.
  • pbFile (string, optional): Name for the created .pb file. Default: "".
  • output_node_names (string, optional): Name of the final operation in the Tensorflow graph. Default: "y_out".

ONNX to NNET

Converts an .onnx model to a .nnet model.

~/ModelVerification.jl/NNet/converters$ python onnx2nnet.py [onnxFile] [nnetFile]
  • onnxFile (string): Path to .onnx file.
  • nnetFile (string, optional): Name for the created .nnet file.

PB to NNET

Converts a .pb model to a .nnet model.

~/ModelVerification.jl/NNet/converters$ python pb2nnet.py [pbFile]
  • pbFile (string): If savedModel is false, it is the path to the frozen graph .pb file. If savedModel is true, it is the path to the savedModel folder, which contains .pb file and variables subdirectory.

PT to ONNX

Converts a .pt model to an .onnx model.

~/ModelVerification.jl/NNet/converters$ python pt2onnx.py --model_path "[path/to/h5/file]" --name_model "[path/to/converted/onnx/file]" --test_conversion [True/False]