Object Detection: YOLOv4

File.cd!(__DIR__)
# for windows JP
# System.shell("chcp 65001")

Mix.install(
  [
    {:axon_interp, "~> 0.1.0"},
    {:cimg, "~> 0.1.14"},
    {:postdnn, "~> 0.1.5"},
    {:kino, "~> 0.7.0"}
  ],
  config: [
    nx: [default_defn_options: [compiler: EXLA]]
  ]
  # system_env: [{"NNINTERP", "Axon"}]
)

0.Original work

Pytorch-YOLOv4

• https://github.com/Tianxiaomo/pytorch-YOLOv4

This note usee the pretraind model converted from above project ;-)

Thanks a lot!!!

Implementation with AxonInterp in Elixir

1.Defining the inference module: YOLOv4

• Model
  yolov4_1_3_608_608_static.onnx

• Pre-processing
  Resize the input image to the size {608,608}, normalize to {0.0,1.0} and transpose NCHW.

• Post-processing
  Filtering Boxes(output[0]) and Scores(output[1]) with Multi-class Non Maximum Suppression.

defmodule YOLOv4 do
  @moduledoc """
  Original work:
    Pytorch-YOLOv4 - https://github.com/Tianxiaomo/pytorch-YOLOv4
  """

  use AxonInterp,
    model: "./model/yolov4_1_3_608_608_static.onnx",
    url:
      "https://github.com/shoz-f/axon_interp/releases/download/0.0.1/yolov4_1_3_608_608_static.onnx",
    inputs: [f32: {1, 3, 608, 608}],
    outputs: [f32: {1, 22743, 1, 4}, f32: {1, 22743, 80}]

  @prepro CImg.builder()
          |> CImg.resize({608, 608})
          |> CImg.to_binary([{:range, {0.0, 1.0}}, :nchw])

  def apply(img) do
    # preprocess
    input0 = CImg.run(@prepro, img)

    # prediction
    outputs =
      session()
      |> AxonInterp.set_input_tensor(0, input0, [:binary])
      |> AxonInterp.invoke()

    # postprocess
    boxes = AxonInterp.get_output_tensor(outputs, 0, [:binary])
    scores = AxonInterp.get_output_tensor(outputs, 1, [:binary])

    PostDNN.non_max_suppression_multi_class(
      {22743, 80},
      boxes,
      scores,
      boxrepr: :corner,
      label: "./model/coco.label"
    )
  end
end

Launch YOLOv4.

# AxonInterp.stop(Resnet18)
YOLOv4.start_link([])

Display the properties of the YOLOv4 model.

AxonInterp.info(YOLOv4)

2.Defining execution module LiveYOLOv4

defmodule LiveYOLOv4 do
  @palette CImg.Util.rand_palette("./model/coco.label")

  def run(path) do
    img = CImg.load(path)

    with {:ok, res} <- YOLOv4.apply(img) do
      IO.inspect(res)

      Enum.reduce(res, CImg.builder(img), &amp;draw_item(&amp;1, &amp;2))
      |> CImg.display_kino(:jpeg)
    end
  end

  defp draw_item({name, boxes}, canvas) do
    color = @palette[name]

    Enum.reduce(boxes, canvas, fn [_score, x1, y1, x2, y2, _index], canvas ->
      [x1, y1, x2, y2] = PostDNN.clamp([x1, y1, x2, y2], {0.0, 1.0})

      CImg.fill_rect(canvas, x1, y1, x2, y2, color, 0.35)
    end)
  end
end

3.Let’s try it

LiveYOLOv4.run("dog.jpg")

Appendix

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