Settings View Source NNInterp

NNInterp is an integrated DNN interpreter for Elixir. It is the successor to TflInterp and OnnxInterp, and allows you to choose a backend framework from "tflite", "onnx-runtime" and "libtorch".

platform

Platform

I have confirmed it works in the following OS environment.

• Windows 10 with Visual C++ 2019
• WSL2/Ubuntu 20.04

requirements

Requirements

cmake 3.13 or later is required.

Visual C++ 2019 for Windows.

installation

Installation

Add the following line to the list of dependencies in mix.exs.

def deps do
  [
    {:nn_interp, "~> 0.1.0"}
  ]
end

basic-usage

Basic Usage

First, select the back-end DNN framework. Set the environment variable NNINTERP to one of the following strings.

• tflite-cpu
• onnx-cpu
• libtorch-cpu

As a little trick, you can put the NNINTERP settings in mix.exs as shown below.

def deps do
  System.put_env("NNINTERP", "onnx-cpu")
  [
    ...
  ]
end

Next, obtain the trained PyTorch model and save it in a directory accessible by your application. The "your-app/priv" directory could be a suitable choice.

$ cp your-trained-model.onnx ./priv

Create a module that interfaces with the deep learning model. This module will require pre-processing and post-processing functionality, in addition to the inference processing provided by NNInterp, as demonstrated in the following example.

At the beginning of your module, include the statement use NNInterp and specify the model path as an optional argument. In the inference section, you will need to set the data input for the model using
NNInterp.set_input_tensor/3, execute the inference with NNInterp.invoke/1, and retrieve the inference results via NNInterp.get_output_tensor/2.

defmodule YourApp.YourModel do
  use NNInterp,
    model: "priv/your-trained-model.onnx"

  def predict(data) do
    # preprocess
    #  to convert the data to be inferred to the input format of the model.
    input_bin = convert-float32-binaries(data)

    # inference
    #  typical I/O data for models is a serialized 32-bit float tensor.
    output_bin = session()
      |> NNInterp.set_input_tensor(0, input_bin)
      |> NNInterp.invoke()
      |> NNInterp.get_output_tensor(0)

    # postprocess
    #  add your post-processing here.
    #  you may need to reshape output_bin to tensor at first.
    tensor = output_bin
      |> Nx.from_binary({:f, 32})
      |> Nx.reshape({size-x, size-y, :auto})

    * your-postprocessing *
    ...
  end
end

demo

Demo

A demo of ResNet18 running on "tflite", "onnx-runtime", or "libtorch" is available on the GitHub for this project. Please refer: https://github.com/shoz-f/nn-interp

Let's enjoy ;-)

license

License

NNInterp is licensed under the Apache License Version 2.0.