Windows ML example

windows_ml_example.py

# Load and predict with ONNX Runtime and a very simple model
# https://onnxruntime.ai/docs/api/python/auto_examples/plot_load_and_predict.html
#
# Install
# https://learn.microsoft.com/en-us/windows/ai/new-windows-ml/get-started?tabs=python>
# pip install win32more
# pip install --pre --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ --extra-index-url https://pypi.org/simple onnxruntime-winml

import asyncio

import numpy.random
import onnxruntime as rt
from onnxruntime.datasets import get_example
from win32more.Microsoft.Windows.AI.MachineLearning import ExecutionProviderCatalog, ExecutionProviderReadyResultState


async def setup():
    catalog = ExecutionProviderCatalog.GetDefault()
    for provider in catalog.FindAllProviders():
        r = await provider.EnsureReadyAsync()
        if r.Status != ExecutionProviderReadyResultState.Success:
            raise OSError(r.ExtendedError.Value, r.DiagnosticText)
        print(f"Provider {provider.Name} {provider.LibraryPath}")
        rt.register_execution_provider_library(provider.Name, provider.LibraryPath)


async def main():
    await setup()

    example1 = get_example("sigmoid.onnx")
    sess = rt.InferenceSession(example1, providers=rt.get_available_providers())

    input_name = sess.get_inputs()[0].name
    print("input name", input_name)
    input_shape = sess.get_inputs()[0].shape
    print("input shape", input_shape)
    input_type = sess.get_inputs()[0].type
    print("input type", input_type)

    output_name = sess.get_outputs()[0].name
    print("output name", output_name)
    output_shape = sess.get_outputs()[0].shape
    print("output shape", output_shape)
    output_type = sess.get_outputs()[0].type
    print("output type", output_type)

    x = numpy.random.random((3, 4, 5))
    x = x.astype(numpy.float32)
    res = sess.run([output_name], {input_name: x})
    print(res)


asyncio.run(main())