View Source OnnxInterp (onnx_interp v0.1.10)

Onnx runtime intepreter for Elixir. Deep Learning inference framework.

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Functions

Adjust NMS result to aspect of the input image. (letterbox)

Get name of backend NN framework.

Ensure that the back-end framework is as expected.

Get the flat binary from the output tensor on the interpreter.

Get the propaty of the model.

Invoke prediction.

run(x) deprecated

Put a flat binary to the input tensor on the interpreter.

Stop the onnx-runtime interpreter.

Ensure that the model matches the back-end framework.

Link to this section Functions

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adjust2letterbox(nms_result, aspect \\ [1.0, 1.0])

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Adjust NMS result to aspect of the input image. (letterbox)

parameters

Parameters:

  • nms_result - NMS result {:ok, result}
  • [rx, ry] - aspect ratio of the input image

Get name of backend NN framework.

Ensure that the back-end framework is as expected.

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get_output_tensor(mod, index, opts \\ [])

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Get the flat binary from the output tensor on the interpreter.

parameters

Parameters

  • mod - modules' names or session.
  • index - index of output tensor in the model

Get the propaty of the model.

parameters

Parameters

  • mod - modules' names

Invoke prediction.

Two modes are toggled depending on the type of input data. One is the stateful mode, in which input/output data are stored as model states. The other mode is stateless, where input/output data is stored in a session structure assigned to the application.

parameters

Parameters

  • mod/session - modules name(stateful) or session structure(stateless).

examples

Examples.

    output_bin = session()  # stateless mode
      |> OnnxInterp.set_input_tensor(0, input_bin)
      |> OnnxInterp.invoke()
      |> OnnxInterp.get_output_tensor(0)
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non_max_suppression_multi_class(mod, arg, boxes, scores, opts \\ [])

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Execute post processing: nms.

parameters

Parameters

  • mod - modules' names
  • num_boxes - number of candidate boxes
  • num_class - number of category class
  • boxes - binaries, serialized boxes tensor[num_boxes][4]; dtype: float32
  • scores - binaries, serialized score tensor[num_boxes][num_class]; dtype: float32
  • opts
    • iou_threshold: - IOU threshold
    • score_threshold: - score cutoff threshold
    • sigma: - soft IOU parameter
    • boxrepr: - type of box representation
      • :center - center pos and width/height
      • :topleft - top-left pos and width/height
      • :corner - top-left and bottom-right corner pos
This function is deprecated. Use invoke/1 instead.
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set_input_tensor(mod, index, bin, opts \\ [])

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Put a flat binary to the input tensor on the interpreter.

parameters

Parameters

  • mod - modules' names or session.
  • index - index of input tensor in the model
  • bin - input data - flat binary, cf. serialized tensor
  • opts - data conversion

Stop the onnx-runtime interpreter.

parameters

Parameters

  • mod - modules' names
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validate_model(model, url)

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Ensure that the model matches the back-end framework.

parameters

Parameters

  • model - path of model file
  • url - download site