Onnx runtime intepreter for Elixir. Deep Learning inference framework.

basic-usage

Basic Usage

You get the trained onnx model and save it in a directory that your application can read. "your-app/priv" may be good choice.

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

Next, you will create a module that interfaces with the deep learning model. The module will need pre-processing and post-processing in addition to inference processing, as in the example following. OnnxInterp provides inference processing only.

You put use OnnxInterp at the beginning of your module, specify the model path as an optional argument. In the inference section, you will put data input to the model (OnnxInterp.set_input_tensor/3), inference execution (OnnxInterp.invoke/1), and inference result retrieval (OnnxInterp.get_output_tensor/2).

defmodule YourApp.YourModel do
  use OnnxInterp, 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 Onnx models is a serialized 32-bit float tensor.
    output_bin =
      __MODULE__
      |> OnnxInterp.set_input_tensor(0, input_bin)
      |> OnnxInterp.invoke()
      |> OnnxInterp.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