mirror of
https://github.com/saymrwulf/onnxruntime.git
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1479 lines
64 KiB
C#
1479 lines
64 KiB
C#
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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using System;
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using System.IO;
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using System.Collections.Generic;
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using System.Linq;
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using System.Runtime.InteropServices;
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using Microsoft.ML.OnnxRuntime.Tensors;
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using System.Threading.Tasks;
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using Xunit;
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using Xunit.Abstractions;
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namespace Microsoft.ML.OnnxRuntime.Tests
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{
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public class InferenceTest
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{
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private const string module = "onnxruntime.dll";
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private const string propertiesFile = "Properties.txt";
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private readonly ITestOutputHelper output;
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public InferenceTest(ITestOutputHelper o)
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{
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this.output = o;
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}
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[Fact]
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public void TestSessionOptions()
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{
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using (SessionOptions opt = new SessionOptions())
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{
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Assert.NotNull(opt);
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// check default values of the properties
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Assert.Equal(ExecutionMode.ORT_SEQUENTIAL, opt.ExecutionMode);
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Assert.True(opt.EnableMemoryPattern);
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Assert.False(opt.EnableProfiling);
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Assert.Equal("onnxruntime_profile_", opt.ProfileOutputPathPrefix);
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Assert.True(opt.EnableCpuMemArena);
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Assert.Equal("", opt.LogId);
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Assert.Equal(LogLevel.Verbose, opt.LogVerbosityLevel);
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Assert.Equal(0, opt.IntraOpNumThreads);
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Assert.Equal(0, opt.InterOpNumThreads);
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Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_ALL, opt.GraphOptimizationLevel);
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// try setting options
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opt.ExecutionMode = ExecutionMode.ORT_PARALLEL;
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Assert.Equal(ExecutionMode.ORT_PARALLEL, opt.ExecutionMode);
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opt.EnableMemoryPattern = false;
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Assert.False(opt.EnableMemoryPattern);
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opt.EnableProfiling = true;
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Assert.True(opt.EnableProfiling);
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Assert.Equal("onnxruntime_profile_", opt.ProfileOutputPathPrefix);
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opt.ProfileOutputPathPrefix = "Ort_P_";
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Assert.Equal("Ort_P_", opt.ProfileOutputPathPrefix);
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opt.EnableCpuMemArena = false;
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Assert.False(opt.EnableCpuMemArena);
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opt.LogId = "MyLogId";
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Assert.Equal("MyLogId", opt.LogId);
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opt.LogVerbosityLevel = LogLevel.Error;
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Assert.Equal(LogLevel.Error, opt.LogVerbosityLevel);
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opt.IntraOpNumThreads = 4;
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Assert.Equal(4, opt.IntraOpNumThreads);
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opt.InterOpNumThreads = 4;
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Assert.Equal(4, opt.InterOpNumThreads);
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opt.GraphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_EXTENDED;
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Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, opt.GraphOptimizationLevel);
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Assert.Throws<OnnxRuntimeException>(() => { opt.GraphOptimizationLevel = (GraphOptimizationLevel)10; });
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opt.AppendExecutionProvider_CPU(1);
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#if USE_DNNL
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opt.AppendExecutionProvider_Dnnl(0);
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#endif
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#if USE_CUDA
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opt.AppendExecutionProvider_CUDA(0);
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#endif
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#if USE_NGRAPH
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opt.AppendExecutionProvider_NGraph("CPU"); //TODO: this API should be refined
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#endif
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#if USE_OPENVINO
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opt.AppendExecutionProvider_OpenVINO(null); //TODO: this won't work, because the native side copies the const char*
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#endif
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#if USE_TENSORRT
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opt.AppendExecutionProvider_Tensorrt(0);
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#endif
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#if USE_NNAPI
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opt.AppendExecutionProvider_Nnapi();
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#endif
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}
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}
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[Fact]
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public void TestRunOptions()
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{
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using (var opt = new RunOptions())
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{
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Assert.NotNull(opt);
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//verify default options
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Assert.False(opt.Terminate);
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Assert.Equal(LogLevel.Verbose, opt.LogVerbosityLevel);
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Assert.Equal("", opt.LogId);
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// try setting options
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opt.Terminate = true;
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Assert.True(opt.Terminate);
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opt.LogVerbosityLevel = LogLevel.Error;
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Assert.Equal(LogLevel.Error, opt.LogVerbosityLevel);
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opt.LogId = "MyLogTag";
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Assert.Equal("MyLogTag", opt.LogId);
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}
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}
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[Fact]
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public void CanCreateAndDisposeSessionWithModelPath()
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{
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string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
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using (var session = new InferenceSession(modelPath))
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{
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Assert.NotNull(session);
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Assert.NotNull(session.InputMetadata);
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Assert.Equal(1, session.InputMetadata.Count); // 1 input node
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Assert.True(session.InputMetadata.ContainsKey("data_0")); // input node name
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Assert.Equal(typeof(float), session.InputMetadata["data_0"].ElementType);
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Assert.True(session.InputMetadata["data_0"].IsTensor);
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var expectedInputDimensions = new int[] { 1, 3, 224, 224 };
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Assert.Equal(expectedInputDimensions.Length, session.InputMetadata["data_0"].Dimensions.Length);
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for (int i = 0; i < expectedInputDimensions.Length; i++)
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{
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Assert.Equal(expectedInputDimensions[i], session.InputMetadata["data_0"].Dimensions[i]);
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}
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Assert.NotNull(session.OutputMetadata);
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Assert.Equal(1, session.OutputMetadata.Count); // 1 output node
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Assert.True(session.OutputMetadata.ContainsKey("softmaxout_1")); // output node name
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Assert.Equal(typeof(float), session.OutputMetadata["softmaxout_1"].ElementType);
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Assert.True(session.OutputMetadata["softmaxout_1"].IsTensor);
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var expectedOutputDimensions = new int[] { 1, 1000, 1, 1 };
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Assert.Equal(expectedOutputDimensions.Length, session.OutputMetadata["softmaxout_1"].Dimensions.Length);
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for (int i = 0; i < expectedOutputDimensions.Length; i++)
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{
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Assert.Equal(expectedOutputDimensions[i], session.OutputMetadata["softmaxout_1"].Dimensions[i]);
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}
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}
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}
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[Theory]
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[InlineData(GraphOptimizationLevel.ORT_DISABLE_ALL, true)]
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[InlineData(GraphOptimizationLevel.ORT_DISABLE_ALL, false)]
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[InlineData(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, true)]
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[InlineData(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, false)]
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private void CanRunInferenceOnAModel(GraphOptimizationLevel graphOptimizationLevel, bool enableParallelExecution)
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{
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string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
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// Set the graph optimization level for this session.
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SessionOptions options = new SessionOptions();
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options.GraphOptimizationLevel = graphOptimizationLevel;
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if (enableParallelExecution) options.ExecutionMode = ExecutionMode.ORT_PARALLEL;
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using (var session = new InferenceSession(modelPath, options))
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{
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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float[] inputData = LoadTensorFromFile(@"bench.in"); // this is the data for only one input tensor for this model
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foreach (var name in inputMeta.Keys)
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{
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Assert.Equal(typeof(float), inputMeta[name].ElementType);
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Assert.True(inputMeta[name].IsTensor);
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var tensor = new DenseTensor<float>(inputData, inputMeta[name].Dimensions);
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container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
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}
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// Run the inference
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using (var results = session.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
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{
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validateRunResults(results);
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}
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// Run Inference with RunOptions
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using (var runOptions = new RunOptions())
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{
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runOptions.LogId = "CsharpTest";
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runOptions.Terminate = false; // TODO: Test terminate = true, it currently crashes
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runOptions.LogVerbosityLevel = LogLevel.Error;
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IReadOnlyCollection<string> outputNames = session.OutputMetadata.Keys.ToList();
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using (var results = session.Run(container, outputNames, runOptions)) // results is an IReadOnlyList<NamedOnnxValue> container
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{
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validateRunResults(results);
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}
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}
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}
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}
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private void validateRunResults(IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results)
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{
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float[] expectedOutput = LoadTensorFromFile(@"bench.expected_out");
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// validate the results
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foreach (var r in results)
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{
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Assert.Equal(1, results.Count);
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Assert.Equal("softmaxout_1", r.Name);
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var resultTensor = r.AsTensor<float>();
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int[] expectedDimensions = { 1, 1000, 1, 1 }; // hardcoded for now for the test data
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Assert.Equal(expectedDimensions.Length, resultTensor.Rank);
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var resultDimensions = resultTensor.Dimensions;
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for (int i = 0; i < expectedDimensions.Length; i++)
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{
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Assert.Equal(expectedDimensions[i], resultDimensions[i]);
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}
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var resultArray = r.AsTensor<float>().ToArray();
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Assert.Equal(expectedOutput.Length, resultArray.Length);
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Assert.Equal(expectedOutput, resultArray, new floatComparer());
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}
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}
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[Fact]
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private void ThrowWrongInputName()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var tensor = tuple.Item3;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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container.Add(NamedOnnxValue.CreateFromTensor<float>("wrong_name", tensor));
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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Assert.Contains("Invalid Feed Input", ex.Message);
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session.Dispose();
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}
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[Fact]
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private void ThrowWrongInputType()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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int[] inputDataInt = inputData.Select(x => (int)x).ToArray();
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var tensor = new DenseTensor<int>(inputDataInt, inputMeta["data_0"].Dimensions);
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container.Add(NamedOnnxValue.CreateFromTensor<int>("data_0", tensor));
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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var msg = ex.ToString().Substring(0, 101);
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// TODO: message is diff in LInux. Use substring match
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Assert.Equal("Microsoft.ML.OnnxRuntime.OnnxRuntimeException: [ErrorCode:InvalidArgument] Unexpected input data type", msg);
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session.Dispose();
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}
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[Fact]
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private void ThrowExtraInputs()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var tensor = tuple.Item3;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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var nov1 = NamedOnnxValue.CreateFromTensor<float>("data_0", tensor);
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var nov2 = NamedOnnxValue.CreateFromTensor<float>("extra", tensor);
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container.Add(nov1);
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container.Add(nov2);
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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Assert.StartsWith("[ErrorCode:InvalidArgument] Invalid Feed Input Name", ex.Message);
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session.Dispose();
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}
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[Fact]
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private void TestMultiThreads()
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{
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var numThreads = 10;
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var loop = 10;
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var tensor = tuple.Item3;
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var expectedOut = tuple.Item4;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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container.Add(NamedOnnxValue.CreateFromTensor<float>("data_0", tensor));
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var tasks = new Task[numThreads];
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for (int i = 0; i < numThreads; i++)
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{
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tasks[i] = Task.Factory.StartNew(() =>
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{
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for (int j = 0; j < loop; j++)
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{
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var resnov = session.Run(container);
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var res = resnov.ToArray()[0].AsTensor<float>().ToArray<float>();
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Assert.Equal(res, expectedOut, new floatComparer());
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}
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});
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};
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Task.WaitAll(tasks);
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session.Dispose();
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}
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private static Dictionary<string, string> GetSkippedModels()
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{
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var skipModels = new Dictionary<string, string>() {
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{ "mxnet_arcface", "Model is an invalid ONNX model"},
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{ "tf_inception_v2", "TODO: Debug failing model, skipping for now" },
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{ "fp16_inception_v1", "16-bit float not supported type in C#." },
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{ "fp16_shufflenet", "16-bit float not supported type in C#." },
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{ "fp16_tiny_yolov2", "16-bit float not supported type in C#." },
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{ "BERT_Squad", "Could not find an implementation for the node bert / embeddings / one_hot:OneHot(9)" },
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{ "mlperf_ssd_mobilenet_300", "Could not find file output_0.pb" },
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{ "tf_resnet_v1_50", "result mismatch when Conv BN Fusion is applied" },
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{ "tf_resnet_v1_101", "result mismatch when Conv BN Fusion is applied" },
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{ "tf_resnet_v1_152", "result mismatch when Conv BN Fusion is applied" },
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{ "mask_rcnn_keras", "Model should be edited to remove the extra outputs" },
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};
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// The following models fails on nocontribops win CI
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var disableContribOpsEnvVar = Environment.GetEnvironmentVariable("DisableContribOps");
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var isContribOpsDisabled = (disableContribOpsEnvVar != null) ? disableContribOpsEnvVar.Equals("ON") : false;
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if (isContribOpsDisabled)
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{
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skipModels["test_tiny_yolov2"] = "Fails when ContribOps is disabled";
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skipModels["mask_rcnn_keras"] = "Pad is not a registered function/op";
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}
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// This model fails on x86 Win CI
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if (System.Environment.Is64BitProcess == false)
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{
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skipModels["test_vgg19"] = "Get preallocated buffer for initializer conv4_4_b_0 failed";
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skipModels["tf_pnasnet_large"] = "Get preallocated buffer for initializer ConvBnFusion_BN_B_cell_5/comb_iter_1/left/bn_sep_7x7_1/beta:0_203 failed";
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skipModels["tf_nasnet_large"] = "Get preallocated buffer for initializer ConvBnFusion_BN_B_cell_11/beginning_bn/beta:0_331 failed";
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skipModels["test_zfnet512"] = "System out of memory";
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skipModels["test_bvlc_reference_caffenet"] = "System out of memory";
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}
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return skipModels;
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}
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public static IEnumerable<object[]> GetModelsForTest()
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{
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var modelsDir = GetTestModelsDir();
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var modelsDirInfo = new DirectoryInfo(modelsDir);
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var skipModels = GetSkippedModels();
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foreach (var opsetDir in modelsDirInfo.EnumerateDirectories())
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{
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//var modelRoot = new DirectoryInfo(Path.Combine(modelsDir, opsetDir.Name));
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foreach (var modelDir in opsetDir.EnumerateDirectories())
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{
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if (!skipModels.ContainsKey(modelDir.Name))
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{
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yield return new object[] { modelDir.Parent.Name, modelDir.Name };
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}
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} //model
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} //opset
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}
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public static IEnumerable<object[]> GetSkippedModelForTest()
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{
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var modelsDir = GetTestModelsDir();
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var modelsDirInfo = new DirectoryInfo(modelsDir);
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var skipModels = GetSkippedModels();
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foreach (var opsetDir in modelsDirInfo.EnumerateDirectories())
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{
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var modelRoot = new DirectoryInfo(Path.Combine(modelsDir, opsetDir.Name));
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foreach (var modelDir in modelRoot.EnumerateDirectories())
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{
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if (skipModels.ContainsKey(modelDir.Name))
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{
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//Console.WriteLine("Model {0} is skipped due to the error: {1}", modelDir.FullName, skipModels[modelDir.Name]);
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yield return new object[] { modelDir.Parent.Name, modelDir.Name };
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}
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}
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}
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}
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[Theory]
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[MemberData(nameof(GetModelsForTest))]
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[MemberData(nameof(GetSkippedModelForTest), Skip = "Skipped due to Error, please fix the error and enable the test")]
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private void TestPreTrainedModels(string opset, string modelName)
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{
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var modelsDir = GetTestModelsDir();
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string onnxModelFileName = null;
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var modelDir = new DirectoryInfo(Path.Combine(modelsDir, opset, modelName));
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try
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{
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var onnxModelNames = modelDir.GetFiles("*.onnx");
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bool validModelFound = false;
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if (onnxModelNames.Length > 0)
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{
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// TODO remove file "._resnet34v2.onnx" from test set
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for (int i = 0; i < onnxModelNames.Length; i++)
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{
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if (onnxModelNames[i].Name != "._resnet34v2.onnx")
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{
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onnxModelNames[0] = onnxModelNames[i];
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validModelFound = true;
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}
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}
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}
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if (validModelFound)
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{
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onnxModelFileName = Path.Combine(modelDir.FullName, onnxModelNames[0].Name);
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}
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else
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{
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var modelNamesList = string.Join(",", onnxModelNames.Select(x => x.ToString()));
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throw new Exception($"Opset {opset} Model {modelName}. Can't determine model file name. Found these :{modelNamesList}");
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}
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using (var session = new InferenceSession(onnxModelFileName))
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{
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var inMeta = session.InputMetadata;
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string testDataDirNamePattern = "test_data*";
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if (opset == "opset9" && modelName == "LSTM_Seq_lens_unpacked")
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{
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testDataDirNamePattern = "seq_lens*"; // discrepency in data directory
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}
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foreach (var testDataDir in modelDir.EnumerateDirectories(testDataDirNamePattern))
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{
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var inputContainer = new List<NamedOnnxValue>();
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var outputContainer = new List<NamedOnnxValue>();
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foreach (var f in testDataDir.EnumerateFiles("input_*.pb"))
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{
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inputContainer.Add(LoadTensorFromFilePb(f.FullName, inMeta));
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}
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foreach (var f in testDataDir.EnumerateFiles("output_*.pb"))
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{
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outputContainer.Add(LoadTensorFromFilePb(f.FullName, session.OutputMetadata));
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}
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using (var resultCollection = session.Run(inputContainer))
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{
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foreach (var result in resultCollection)
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{
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Assert.True(session.OutputMetadata.ContainsKey(result.Name));
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var outputMeta = session.OutputMetadata[result.Name];
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NamedOnnxValue outputValue = null;
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foreach (var o in outputContainer)
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{
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if (o.Name == result.Name)
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{
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outputValue = o;
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break;
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}
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}
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if (outputValue == null)
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{
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outputValue = outputContainer.First(); // in case the output data file does not contain the name
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}
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if (outputMeta.IsTensor)
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{
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if (outputMeta.ElementType == typeof(float))
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{
|
|
Assert.Equal(result.AsTensor<float>(), outputValue.AsTensor<float>(), new floatComparer());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(int))
|
|
{
|
|
Assert.Equal(result.AsTensor<int>(), outputValue.AsTensor<int>(), new ExactComparer<int>());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(uint))
|
|
{
|
|
Assert.Equal(result.AsTensor<uint>(), outputValue.AsTensor<uint>(), new ExactComparer<uint>());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(short))
|
|
{
|
|
Assert.Equal(result.AsTensor<short>(), outputValue.AsTensor<short>(), new ExactComparer<short>());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(ushort))
|
|
{
|
|
Assert.Equal(result.AsTensor<ushort>(), outputValue.AsTensor<ushort>(), new ExactComparer<ushort>());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(long))
|
|
{
|
|
Assert.Equal(result.AsTensor<long>(), outputValue.AsTensor<long>(), new ExactComparer<long>());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(ulong))
|
|
{
|
|
Assert.Equal(result.AsTensor<ulong>(), outputValue.AsTensor<ulong>(), new ExactComparer<ulong>());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(byte))
|
|
{
|
|
Assert.Equal(result.AsTensor<byte>(), outputValue.AsTensor<byte>(), new ExactComparer<byte>());
|
|
}
|
|
else if (outputMeta.ElementType == typeof(bool))
|
|
{
|
|
Assert.Equal(result.AsTensor<bool>(), outputValue.AsTensor<bool>(), new ExactComparer<bool>());
|
|
}
|
|
else
|
|
{
|
|
Assert.True(false, "The TestPretrainedModels does not yet support output of type " + nameof(outputMeta.ElementType));
|
|
}
|
|
}
|
|
else
|
|
{
|
|
Assert.True(false, "TestPretrainedModel cannot handle non-tensor outputs yet");
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
catch (Exception ex)
|
|
{
|
|
var msg = $"Opset {opset}, Model {modelName}: ModelFile = {onnxModelFileName} error = {ex.Message}";
|
|
throw new Exception(msg + "\n" + ex.StackTrace);
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestOverridableInitializerMetadata()
|
|
{
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "overridable_initializer.onnx");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
Assert.Equal(2, session.InputMetadata.Count);
|
|
Assert.True(session.InputMetadata.ContainsKey("Label"));
|
|
Assert.True(session.InputMetadata.ContainsKey("F2"));
|
|
|
|
Assert.Equal(1, session.OverridableInitializerMetadata.Count);
|
|
Assert.True(session.OverridableInitializerMetadata.ContainsKey("F1"));
|
|
Assert.True(session.OverridableInitializerMetadata["F1"].IsTensor);
|
|
Assert.Equal(typeof(float), session.OverridableInitializerMetadata["F1"].ElementType);
|
|
Assert.Equal(2, session.OverridableInitializerMetadata["F1"].Dimensions.Length);
|
|
Assert.Equal(1, session.OverridableInitializerMetadata["F1"].Dimensions[0]);
|
|
Assert.Equal(1, session.OverridableInitializerMetadata["F1"].Dimensions[1]);
|
|
|
|
var container = new List<NamedOnnxValue>();
|
|
var Label_input = new DenseTensor<bool>(new bool[] { true }, new int[] { 1, 1 });
|
|
container.Add(NamedOnnxValue.CreateFromTensor("Label", Label_input));
|
|
|
|
var F2_input = new DenseTensor<string>(new string[] { "f2_string" }, new int[] { 1, 1 });
|
|
container.Add(NamedOnnxValue.CreateFromTensor("F2", F2_input));
|
|
|
|
var F1_initializer = new DenseTensor<float>(new float[] { 2.0f }, new int[] { 1, 1 });
|
|
container.Add(NamedOnnxValue.CreateFromTensor("F1", F1_initializer));
|
|
|
|
using (var result = session.Run(container))
|
|
{
|
|
var resultMap = new Dictionary<string, NamedOnnxValue>();
|
|
|
|
foreach (var output in result)
|
|
{
|
|
resultMap[output.Name] = output;
|
|
}
|
|
|
|
Assert.True(resultMap.ContainsKey("Label0"));
|
|
Assert.True(resultMap.ContainsKey("F20"));
|
|
Assert.True(resultMap.ContainsKey("F11"));
|
|
|
|
var overriddenInitializer = resultMap["F11"].AsTensor<float>();
|
|
Assert.NotNull(overriddenInitializer);
|
|
Assert.True(overriddenInitializer.SequenceEqual(F1_initializer));
|
|
}
|
|
}
|
|
}
|
|
|
|
[SkipNonPackageTests]
|
|
private void TestRegisterCustomOpLibrary()
|
|
{
|
|
using (var option = new SessionOptions())
|
|
{
|
|
string libName = "custom_op_library.dll";
|
|
string modelPath = "custom_op_test.onnx";
|
|
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
|
|
{
|
|
libName = "custom_op_library.dll";
|
|
}
|
|
else if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
|
|
{
|
|
libName = "libcustom_op_library.so";
|
|
}
|
|
else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
|
|
{
|
|
libName = "libcustom_op_library.dylib";
|
|
}
|
|
|
|
string libFullPath = Path.Combine(Directory.GetCurrentDirectory(), libName);
|
|
Assert.True(File.Exists(libFullPath), $"Expected lib {libFullPath} does not exist.");
|
|
|
|
try
|
|
{
|
|
option.RegisterCustomOpLibrary(libFullPath);
|
|
}
|
|
catch (Exception ex)
|
|
{
|
|
var msg = $"Failed to load custom op library {libFullPath}, error = {ex.Message}";
|
|
throw new Exception(msg + "\n" + ex.StackTrace);
|
|
}
|
|
|
|
|
|
using (var session = new InferenceSession(modelPath, option))
|
|
{
|
|
var inputContainer = new List<NamedOnnxValue>();
|
|
inputContainer.Add(NamedOnnxValue.CreateFromTensor<float>("input_1",
|
|
new DenseTensor<float>(
|
|
new float[]
|
|
{
|
|
1.1f, 2.2f, 3.3f, 4.4f, 5.5f,
|
|
6.6f, 7.7f, 8.8f, 9.9f, 10.0f,
|
|
11.1f, 12.2f, 13.3f, 14.4f, 15.5f
|
|
},
|
|
new int[] { 3, 5 }
|
|
)));
|
|
|
|
inputContainer.Add(NamedOnnxValue.CreateFromTensor<float>("input_2",
|
|
new DenseTensor<float>(
|
|
new float[]
|
|
{
|
|
15.5f, 14.4f, 13.3f, 12.2f, 11.1f,
|
|
10.0f, 9.9f, 8.8f, 7.7f, 6.6f,
|
|
5.5f, 4.4f, 3.3f, 2.2f, 1.1f
|
|
},
|
|
new int[] { 3, 5 }
|
|
)));
|
|
|
|
using (var result = session.Run(inputContainer))
|
|
{
|
|
Assert.Equal("output", result.First().Name);
|
|
var tensorOut = result.First().AsTensor<int>();
|
|
|
|
var expectedOut = new DenseTensor<int>(
|
|
new int[]
|
|
{
|
|
17, 17, 17, 17, 17,
|
|
17, 18, 18, 18, 17,
|
|
17, 17, 17, 17, 17
|
|
},
|
|
new int[] { 3, 5 }
|
|
);
|
|
Assert.True(tensorOut.SequenceEqual(expectedOut));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestSymbolicDimsMetadata()
|
|
{
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "capi_symbolic_dims.onnx");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var inputs = session.InputMetadata;
|
|
var outputs = session.OutputMetadata;
|
|
|
|
Assert.Equal(2, inputs.Count);
|
|
Assert.Equal(1, session.OutputMetadata.Count);
|
|
Assert.True(inputs.ContainsKey("A"));
|
|
Assert.True(inputs.ContainsKey("B"));
|
|
Assert.True(outputs.ContainsKey("C"));
|
|
|
|
var inputA = inputs["A"];
|
|
var inputB = inputs["B"];
|
|
var outputC = outputs["C"];
|
|
|
|
// dimension values and any symbolic dimension info should have the same length
|
|
Assert.Equal(inputA.Dimensions.Length, inputA.SymbolicDimensions.Length);
|
|
Assert.Equal(inputB.Dimensions.Length, inputB.SymbolicDimensions.Length);
|
|
Assert.Equal(outputC.Dimensions.Length, outputC.SymbolicDimensions.Length);
|
|
|
|
Assert.Equal(inputA.Dimensions, new int[] { -1, 2 });
|
|
Assert.Equal(inputA.SymbolicDimensions, new string[] { "n", "" });
|
|
Assert.Equal(inputB.Dimensions, new int[] { -1 });
|
|
Assert.Equal(inputB.SymbolicDimensions, new string[] { "m" });
|
|
Assert.Equal(outputC.Dimensions, new int[] { -1 });
|
|
Assert.Equal(outputC.SymbolicDimensions, new string[] { "" }); // unnamed symbolic dim
|
|
}
|
|
}
|
|
|
|
|
|
[Fact]
|
|
private void TestModelInputFloat()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_FLOAT.pb");
|
|
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<float>(new float[] { 1.0f, 2.0f, -3.0f, float.MinValue, float.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<float>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputBOOL()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_BOOL.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<bool>(new bool[] { true, false, true, false, true }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<bool>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputINT32()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_INT32.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<int>(new int[] { 1, -2, -3, int.MinValue, int.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<int>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputDOUBLE()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_DOUBLE.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<double>(new double[] { 1.0, 2.0, -3.0, 5, 5 }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<double>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputSTRING()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_STRING.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<string>(new string[] {
|
|
"hello",
|
|
"École élémentaire",
|
|
"mit freundlichen grüßen",
|
|
"Понедельник",
|
|
"最好的问候,"+
|
|
"नमस्ते," +
|
|
"こんにちは," +
|
|
"안녕하세요"
|
|
}, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<string>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputINT8()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_INT8.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<sbyte>(new sbyte[] { 1, 2, -3, sbyte.MinValue, sbyte.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<sbyte>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputUINT8()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_UINT8.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<byte>(new byte[] { 1, 2, 3, byte.MinValue, byte.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<byte>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputUINT16()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_UINT16.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<UInt16>(new UInt16[] { 1, 2, 3, UInt16.MinValue, UInt16.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<UInt16>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputINT16()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_INT16.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<Int16>(new Int16[] { 1, 2, 3, Int16.MinValue, Int16.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<Int16>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputINT64()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_INT64.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<Int64>(new Int64[] { 1, 2, -3, Int64.MinValue, Int64.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<Int64>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelInputUINT32()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_UINT32.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<UInt32>(new UInt32[] { 1, 2, 3, UInt32.MinValue, UInt32.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<UInt32>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
[Fact]
|
|
private void TestModelInputUINT64()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_UINT64.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<UInt64>(new UInt64[] { 1, 2, 3, UInt64.MinValue, UInt64.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<UInt64>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact(Skip = "FLOAT16 not available in C#")]
|
|
private void TestModelInputFLOAT16()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_FLOAT16.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<float>(new float[] { 1.0f, 2.0f, -3.0f, float.MinValue, float.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<float>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelSequenceOfMapIntFloat()
|
|
{
|
|
// test model trained using lightgbm classifier
|
|
// produces 2 named outputs
|
|
// "label" is a tensor,
|
|
// "probabilities" is a sequence<map<int64, float>>
|
|
// https://github.com/onnx/sklearn-onnx/blob/master/docs/examples/plot_pipeline_lightgbm.py
|
|
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_sequence_map_int_float.pb");
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
|
|
var outMeta = session.OutputMetadata;
|
|
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, outMeta["label"].OnnxValueType);
|
|
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, outMeta["probabilities"].OnnxValueType);
|
|
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<float>(new float[] { 5.8f, 2.8f }, new int[] { 1, 2 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
|
|
using (var outputs = session.Run(container))
|
|
{
|
|
// first output is a tensor containing label
|
|
var outNode1 = outputs.ElementAtOrDefault(0);
|
|
Assert.Equal("label", outNode1.Name);
|
|
|
|
// try-cast as a tensor
|
|
var outLabelTensor = outNode1.AsTensor<Int64>();
|
|
|
|
// Label 1 should have highest probaility
|
|
Assert.Equal(1, outLabelTensor[0]);
|
|
|
|
// second output is a sequence<map<int64, float>>
|
|
// try-cast to an sequence of NOV
|
|
var outNode2 = outputs.ElementAtOrDefault(1);
|
|
Assert.Equal("probabilities", outNode2.Name);
|
|
|
|
// try-cast to an sequence of NOV
|
|
var seq = outNode2.AsEnumerable<NamedOnnxValue>();
|
|
|
|
// try-cast first element in sequence to map/dictionary type
|
|
if (System.Environment.Is64BitProcess)
|
|
{
|
|
var map = seq.First().AsDictionary<Int64, float>();
|
|
Assert.Equal(0.25938290, map[0], 6);
|
|
Assert.Equal(0.40904793, map[1], 6);
|
|
Assert.Equal(0.33156919, map[2], 6);
|
|
}
|
|
else // 32-bit
|
|
{
|
|
var map = seq.First().AsDictionary<long, float>();
|
|
Assert.Equal(0.25938290, map[0], 6);
|
|
Assert.Equal(0.40904793, map[1], 6);
|
|
Assert.Equal(0.33156919, map[2], 6);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelSequenceOfMapStringFloat()
|
|
{
|
|
// test model trained using lightgbm classifier
|
|
// produces 2 named outputs
|
|
// "label" is a tensor,
|
|
// "probabilities" is a sequence<map<int64, float>>
|
|
// https://github.com/onnx/sklearn-onnx/blob/master/docs/examples/plot_pipeline_lightgbm.py
|
|
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_sequence_map_string_float.pb");
|
|
|
|
using (var session = new InferenceSession(modelPath))
|
|
{
|
|
var outMeta = session.OutputMetadata;
|
|
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, outMeta["label"].OnnxValueType);
|
|
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, outMeta["probabilities"].OnnxValueType);
|
|
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<float>(new float[] { 5.8f, 2.8f }, new int[] { 1, 2 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
|
|
using (var outputs = session.Run(container))
|
|
{
|
|
// first output is a tensor containing label
|
|
var outNode1 = outputs.ElementAtOrDefault(0);
|
|
Assert.Equal("label", outNode1.Name);
|
|
|
|
// try-cast as a tensor
|
|
var outLabelTensor = outNode1.AsTensor<string>();
|
|
|
|
// Label 1 should have highest probaility
|
|
Assert.Equal("1", outLabelTensor[0]);
|
|
|
|
// second output is a sequence<map<int64, float>>
|
|
// try-cast to an sequence of NOV
|
|
var outNode2 = outputs.ElementAtOrDefault(1);
|
|
Assert.Equal("probabilities", outNode2.Name);
|
|
|
|
// try-cast to an sequence of NOV
|
|
var seq = outNode2.AsEnumerable<NamedOnnxValue>();
|
|
|
|
// try-cast first element in sequence to map/dictionary type
|
|
var map = seq.First().AsDictionary<string, float>();
|
|
//verify values are valid
|
|
Assert.Equal(0.25938290, map["0"], 6);
|
|
Assert.Equal(0.40904793, map["1"], 6);
|
|
Assert.Equal(0.33156919, map["2"], 6);
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestModelSerialization()
|
|
{
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
|
|
string modelOutputPath = Path.Combine(Directory.GetCurrentDirectory(), "optimized-squeezenet.onnx");
|
|
// Set the optimized model file path to assert that no exception are thrown.
|
|
SessionOptions options = new SessionOptions();
|
|
options.OptimizedModelFilePath = modelOutputPath;
|
|
options.GraphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_BASIC;
|
|
var session = new InferenceSession(modelPath, options);
|
|
Assert.NotNull(session);
|
|
Assert.True(File.Exists(modelOutputPath));
|
|
}
|
|
|
|
[GpuFact]
|
|
private void TestGpu()
|
|
{
|
|
var gpu = Environment.GetEnvironmentVariable("TESTONGPU");
|
|
var tuple = OpenSessionSqueezeNet(0); // run on deviceID 0
|
|
float[] expectedOutput = LoadTensorFromFile(@"bench.expected_out");
|
|
|
|
using (var session = tuple.Item1)
|
|
{
|
|
var inputData = tuple.Item2;
|
|
var tensor = tuple.Item3;
|
|
var inputMeta = session.InputMetadata;
|
|
var container = new List<NamedOnnxValue>();
|
|
container.Add(NamedOnnxValue.CreateFromTensor<float>("data_0", tensor));
|
|
var res = session.Run(container);
|
|
var resultArray = res.First().AsTensor<float>().ToArray();
|
|
Assert.Equal(expectedOutput, resultArray, new floatComparer());
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
private void TestInferenceSessionWithByteArray()
|
|
{
|
|
// model takes 1x5 input of fixed type, echoes back
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "test_types_FLOAT.pb");
|
|
byte[] modelData = File.ReadAllBytes(modelPath);
|
|
|
|
using (var session = new InferenceSession(modelData))
|
|
{
|
|
var container = new List<NamedOnnxValue>();
|
|
var tensorIn = new DenseTensor<float>(new float[] { 1.0f, 2.0f, -3.0f, float.MinValue, float.MaxValue }, new int[] { 1, 5 });
|
|
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
|
|
container.Add(nov);
|
|
using (var res = session.Run(container))
|
|
{
|
|
var tensorOut = res.First().AsTensor<float>();
|
|
Assert.True(tensorOut.SequenceEqual(tensorIn));
|
|
}
|
|
}
|
|
}
|
|
|
|
[DllImport("kernel32", SetLastError = true)]
|
|
static extern IntPtr LoadLibrary(string lpFileName);
|
|
|
|
[DllImport("kernel32", CharSet = CharSet.Ansi)]
|
|
static extern UIntPtr GetProcAddress(IntPtr hModule, string procName);
|
|
|
|
[Fact]
|
|
private void VerifyNativeMethodsExist()
|
|
{
|
|
// Check for external API changes
|
|
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
|
|
return;
|
|
var entryPointNames = new[]{
|
|
"OrtGetApiBase",
|
|
"OrtSessionOptionsAppendExecutionProvider_CPU"
|
|
#if USE_DNNL
|
|
,"OrtSessionOptionsAppendExecutionProvider_Dnnl"
|
|
#endif
|
|
#if USE_CUDA
|
|
,"OrtSessionOptionsAppendExecutionProvider_CUDA"
|
|
#endif
|
|
#if USE_NGRAPH
|
|
,"OrtSessionOptionsAppendExecutionProvider_NGraph"
|
|
#endif
|
|
#if USE_OPENVINO
|
|
,"OrtSessionOptionsAppendExecutionProvider_OpenVINO"
|
|
#endif
|
|
#if USE_TENSORRT
|
|
,"OrtSessionOptionsAppendExecutionProvider_Tensorrt"
|
|
#endif
|
|
#if USE_NNAPI
|
|
,"OrtSessionOptionsAppendExecutionProvider_Nnapi"
|
|
#endif
|
|
};
|
|
|
|
var hModule = LoadLibrary(module);
|
|
foreach (var ep in entryPointNames)
|
|
{
|
|
var x = GetProcAddress(hModule, ep);
|
|
Assert.False(x == UIntPtr.Zero, $"Entrypoint {ep} not found in module {module}");
|
|
}
|
|
}
|
|
|
|
static string GetTestModelsDir()
|
|
{
|
|
// get build directory, append downloaded models location
|
|
var cwd = Directory.GetCurrentDirectory();
|
|
var props = File.ReadAllLines(Path.Combine(cwd, propertiesFile));
|
|
var modelsRelDir = Path.Combine(props[0].Split('=')[1].Trim());
|
|
var modelsDir = Path.Combine(cwd, @"../../..", modelsRelDir, "models");
|
|
return modelsDir;
|
|
}
|
|
|
|
static float[] LoadTensorFromFile(string filename, bool skipheader = true)
|
|
{
|
|
var tensorData = new List<float>();
|
|
|
|
// read data from file
|
|
using (var inputFile = new System.IO.StreamReader(filename))
|
|
{
|
|
if (skipheader)
|
|
inputFile.ReadLine(); //skip the input name
|
|
string[] dataStr = inputFile.ReadLine().Split(new char[] { ',', '[', ']', ' ' }, StringSplitOptions.RemoveEmptyEntries);
|
|
for (int i = 0; i < dataStr.Length; i++)
|
|
{
|
|
tensorData.Add(Single.Parse(dataStr[i]));
|
|
}
|
|
}
|
|
|
|
return tensorData.ToArray();
|
|
}
|
|
|
|
|
|
private enum TensorElementType
|
|
{
|
|
Float = 1,
|
|
UInt8 = 2,
|
|
Int8 = 3,
|
|
UInt16 = 4,
|
|
Int16 = 5,
|
|
Int32 = 6,
|
|
Int64 = 7,
|
|
String = 8,
|
|
Bool = 9,
|
|
Float16 = 10,
|
|
Double = 11,
|
|
UInt32 = 12,
|
|
UInt64 = 13,
|
|
Complex64 = 14,
|
|
Complex128 = 15,
|
|
BFloat16 = 16,
|
|
DataTypeMax = 17
|
|
}
|
|
|
|
private static void GetTypeAndWidth(TensorElementType elemType, out Type type, out int width)
|
|
{
|
|
switch (elemType)
|
|
{
|
|
case TensorElementType.Float:
|
|
type = typeof(float);
|
|
width = sizeof(float);
|
|
break;
|
|
case TensorElementType.Double:
|
|
type = typeof(double);
|
|
width = sizeof(double);
|
|
break;
|
|
case TensorElementType.Int16:
|
|
type = typeof(short);
|
|
width = sizeof(short);
|
|
break;
|
|
case TensorElementType.UInt16:
|
|
type = typeof(ushort);
|
|
width = sizeof(ushort);
|
|
break;
|
|
case TensorElementType.Int32:
|
|
type = typeof(int);
|
|
width = sizeof(int);
|
|
break;
|
|
case TensorElementType.UInt32:
|
|
type = typeof(uint);
|
|
width = sizeof(uint);
|
|
break;
|
|
case TensorElementType.Int64:
|
|
type = typeof(long);
|
|
width = sizeof(long);
|
|
break;
|
|
case TensorElementType.UInt64:
|
|
type = typeof(ulong);
|
|
width = sizeof(ulong);
|
|
break;
|
|
case TensorElementType.UInt8:
|
|
type = typeof(byte);
|
|
width = sizeof(byte);
|
|
break;
|
|
case TensorElementType.Int8:
|
|
type = typeof(sbyte);
|
|
width = sizeof(sbyte);
|
|
break;
|
|
case TensorElementType.String:
|
|
type = typeof(byte);
|
|
width = sizeof(byte);
|
|
break;
|
|
case TensorElementType.Bool:
|
|
type = typeof(bool);
|
|
width = sizeof(bool);
|
|
break;
|
|
default:
|
|
type = null;
|
|
width = 0;
|
|
break;
|
|
}
|
|
}
|
|
static NamedOnnxValue LoadTensorFromFilePb(string filename, IReadOnlyDictionary<string, NodeMetadata> nodeMetaDict)
|
|
{
|
|
var file = File.OpenRead(filename);
|
|
var tensor = Onnx.TensorProto.Parser.ParseFrom(file);
|
|
file.Close();
|
|
|
|
Type tensorElemType = null;
|
|
int width = 0;
|
|
GetTypeAndWidth((TensorElementType)tensor.DataType, out tensorElemType, out width);
|
|
var intDims = new int[tensor.Dims.Count];
|
|
for (int i = 0; i < tensor.Dims.Count; i++)
|
|
{
|
|
intDims[i] = (int)tensor.Dims[i];
|
|
}
|
|
|
|
NodeMetadata nodeMeta = null;
|
|
string nodeName = "";
|
|
if (nodeMetaDict.Count == 1)
|
|
{
|
|
nodeMeta = nodeMetaDict.Values.First();
|
|
nodeName = nodeMetaDict.Keys.First(); // valid for single node input
|
|
}
|
|
else if (nodeMetaDict.Count > 1)
|
|
{
|
|
if (tensor.Name != "")
|
|
{
|
|
nodeMeta = nodeMetaDict[tensor.Name];
|
|
nodeName = tensor.Name;
|
|
}
|
|
else
|
|
{
|
|
bool matchfound = false;
|
|
// try to find from matching type and shape
|
|
foreach (var key in nodeMetaDict.Keys)
|
|
{
|
|
var meta = nodeMetaDict[key];
|
|
if (tensorElemType == meta.ElementType && tensor.Dims.Count == meta.Dimensions.Length)
|
|
{
|
|
int i = 0;
|
|
for (; i < meta.Dimensions.Length; i++)
|
|
{
|
|
if (meta.Dimensions[i] != -1 && meta.Dimensions[i] != intDims[i])
|
|
{
|
|
break;
|
|
}
|
|
}
|
|
if (i >= meta.Dimensions.Length)
|
|
{
|
|
matchfound = true;
|
|
nodeMeta = meta;
|
|
nodeName = key;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
if (!matchfound)
|
|
{
|
|
// throw error
|
|
throw new Exception("No Matching Tensor found in InputOutputMetadata corresponding to the serliazed tensor loaded from " + filename);
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
// throw error
|
|
throw new Exception("While reading the serliazed tensor loaded from " + filename + ", metaDataDict has 0 elements");
|
|
}
|
|
|
|
Assert.True(nodeMeta.IsTensor, "LoadTensorFromFile can load Tensor types only");
|
|
//TODO: support other types when models are available in Onnx model zoo/ test data
|
|
|
|
Assert.Equal(tensorElemType, nodeMeta.ElementType);
|
|
Assert.Equal(nodeMeta.Dimensions.Length, tensor.Dims.Count);
|
|
for (int i = 0; i < nodeMeta.Dimensions.Length; i++)
|
|
{
|
|
Assert.True((nodeMeta.Dimensions[i] == -1) || (nodeMeta.Dimensions[i] == intDims[i]));
|
|
}
|
|
|
|
if (nodeMeta.ElementType == typeof(float))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<float>(nodeName, tensor.RawData.ToArray(), sizeof(float), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(double))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<double>(nodeName, tensor.RawData.ToArray(), sizeof(double), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(int))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<int>(nodeName, tensor.RawData.ToArray(), sizeof(int), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(uint))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<uint>(nodeName, tensor.RawData.ToArray(), sizeof(uint), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(long))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<long>(nodeName, tensor.RawData.ToArray(), sizeof(long), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(ulong))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<ulong>(nodeName, tensor.RawData.ToArray(), sizeof(ulong), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(short))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<short>(nodeName, tensor.RawData.ToArray(), sizeof(short), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(ushort))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<ushort>(nodeName, tensor.RawData.ToArray(), sizeof(ushort), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(byte))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<byte>(nodeName, tensor.RawData.ToArray(), sizeof(byte), intDims);
|
|
}
|
|
else if (nodeMeta.ElementType == typeof(bool))
|
|
{
|
|
return CreateNamedOnnxValueFromRawData<bool>(nodeName, tensor.RawData.ToArray(), sizeof(bool), intDims);
|
|
}
|
|
else
|
|
{
|
|
//TODO: Add support for remaining types
|
|
Assert.True(false, "Tensors of type " + nameof(nodeMeta.ElementType) + " not currently supported in the LoadTensorFromFile");
|
|
return null;
|
|
}
|
|
}
|
|
|
|
static NamedOnnxValue CreateNamedOnnxValueFromRawData<T>(string name, byte[] rawData, int elemWidth, int[] dimensions)
|
|
{
|
|
T[] floatArr = new T[rawData.Length / elemWidth];
|
|
Buffer.BlockCopy(rawData, 0, floatArr, 0, rawData.Length);
|
|
var dt = new DenseTensor<T>(floatArr, dimensions);
|
|
return NamedOnnxValue.CreateFromTensor<T>(name, dt);
|
|
}
|
|
|
|
static Tuple<InferenceSession, float[], DenseTensor<float>, float[]> OpenSessionSqueezeNet(int? cudaDeviceId = null)
|
|
{
|
|
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
|
|
var option = new SessionOptions();
|
|
#if USE_CUDA
|
|
if (cudaDeviceId.HasValue)
|
|
{
|
|
option = SessionOptions.MakeSessionOptionWithCudaProvider(cudaDeviceId.Value);
|
|
}
|
|
#endif
|
|
var session = (cudaDeviceId.HasValue)
|
|
? new InferenceSession(modelPath, option)
|
|
: new InferenceSession(modelPath);
|
|
float[] inputData = LoadTensorFromFile(@"bench.in");
|
|
float[] expectedOutput = LoadTensorFromFile(@"bench.expected_out");
|
|
var inputMeta = session.InputMetadata;
|
|
var tensor = new DenseTensor<float>(inputData, inputMeta["data_0"].Dimensions);
|
|
return new Tuple<InferenceSession, float[], DenseTensor<float>, float[]>(session, inputData, tensor, expectedOutput);
|
|
}
|
|
|
|
class floatComparer : IEqualityComparer<float>
|
|
{
|
|
private float atol = 1e-3f;
|
|
private float rtol = 1.7e-2f;
|
|
|
|
public bool Equals(float x, float y)
|
|
{
|
|
return Math.Abs(x - y) <= (atol + rtol * Math.Abs(y));
|
|
}
|
|
public int GetHashCode(float x)
|
|
{
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
class ExactComparer<T> : IEqualityComparer<T>
|
|
{
|
|
public bool Equals(T x, T y)
|
|
{
|
|
return x.Equals(y);
|
|
}
|
|
public int GetHashCode(T x)
|
|
{
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
|
|
private class GpuFact : FactAttribute
|
|
{
|
|
public GpuFact()
|
|
{
|
|
var testOnGpu = System.Environment.GetEnvironmentVariable("TESTONGPU");
|
|
if (testOnGpu == null || !testOnGpu.Equals("ON"))
|
|
{
|
|
Skip = "GPU testing not enabled";
|
|
}
|
|
}
|
|
}
|
|
|
|
private class SkipNonPackageTests : FactAttribute
|
|
{
|
|
public SkipNonPackageTests()
|
|
{
|
|
var skipNonPackageTests = System.Environment.GetEnvironmentVariable("SKIPNONPACKAGETESTS");
|
|
if (skipNonPackageTests != null && skipNonPackageTests.Equals("ON"))
|
|
{
|
|
Skip = "Test skipped while testing the package as it is not within the scope";
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
}
|