Image Classification Server B

Mix.install(
  [
    {:nx, "~> 0.9", override: true},
    {:exla, "~> 0.9"},
    {:axon_onnx, "~> 0.4", git: "https://github.com/mortont/axon_onnx/"},
    {:kino, "~> 0.14"}
  ],
  config: [nx: [default_backend: EXLA.Backend]]
)

Load models

Nx.Defn.default_options(compiler: EXLA)
Nx.global_default_backend(EXLA.Backend)

classes = ["clear", "cloudy"]

model_path = "/tmp/sorabatake/efficientnet_v2_m.onnx"

{model, params} = AxonOnnx.import(model_path)

Serve

defmodule EfficientNetV2 do
  import Nx.Defn

  defn normalize(tensor) do
    (tensor - Nx.tensor([0.485, 0.456, 0.406])) / Nx.tensor([0.229, 0.224, 0.225])
  end

  defn transform_for_input(img_tensor) do
    (img_tensor / 255)
    |> normalize()
    |> Nx.transpose(axes: [2, 0, 1])
  end

  defn get_top_class_index(outputs) do
    Nx.argmax(outputs, axis: 1)
  end

  defn softmax(tensor) do
    Nx.exp(tensor) / Nx.sum(Nx.exp(tensor), axes: [-1], keep_axes: true)
  end

  defn get_top_class_score(outputs) do
    outputs
    |> softmax()
    |> Nx.reduce_max(axes: [-1])
  end

  def preprocess(tensor_list, batch_size) do
    tensor_list
    |> Enum.map(fn tensor ->
      transform_for_input(tensor)
    end)
    |> Nx.Batch.stack()
    |> Nx.Batch.pad(batch_size - Enum.count(tensor_list))
  end

  def postprocess(outputs, classes) do
    predicted_classes = get_top_class_index(outputs)
    predicted_scores = get_top_class_score(outputs)

    output_size =
      outputs
      |> Nx.shape()
      |> elem(0)

    0..(output_size - 1)
    |> Enum.to_list()
    |> Enum.map(fn index ->
      predicted_class =
        predicted_classes[index]
        |> Nx.to_number()
        |> then(&Enum.at(classes, &1))

      predicted_score = Nx.to_number(predicted_scores[index])

      %{
        predicted_class: predicted_class,
        predicted_score: predicted_score
      }
    end)
  end

  def predict(input_batch, model, params) do
    Axon.predict(model, params, input_batch)
  end
end

frame = Kino.Frame.new()

batch_size = 4

serving =
  Nx.Serving.new(
    fn _ ->
      fn input_batch ->
        EfficientNetV2.predict(input_batch, model, params)
      end
    end,
    compiler: EXLA
  )
  |> Nx.Serving.process_options(batch_size: batch_size)
  |> Nx.Serving.client_preprocessing(fn tensor_list ->
    input_batch = EfficientNetV2.preprocess(tensor_list, batch_size)
    {input_batch, :client_info}
  end)
  |> Nx.Serving.client_postprocessing(fn {outputs, _metadata}, _multi? ->
    predictions =
      EfficientNetV2.postprocess(outputs, classes)

    predictions
    |> Enum.map(fn prediction ->
      prediction.predicted_class
    end)
    |> Enum.join(", ")
    |> then(&Kino.Frame.render(frame, &1))

    predictions
  end)

dummy_tensor =
  0
  |> Nx.broadcast({224, 224, 3})
  |> Nx.as_type(:u8)

Nx.Serving.run(serving, [dummy_tensor])

Kino.start_child({Nx.Serving, serving: serving, name: ImageClassification})

Nx.Serving.batched_run({:distributed, ImageClassification}, [dummy_tensor])

Get connection info

{node(), Node.get_cookie()}