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InferenceTest.cs
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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
using System;
using System.IO;
using System.Collections.Generic;
using System.Linq;
using System.Runtime.InteropServices;
using Microsoft.ML.OnnxRuntime.Tensors;
using System.Threading.Tasks;
using Xunit;
using Xunit.Abstractions;
namespace Microsoft.ML.OnnxRuntime.Tests
{
public class InferenceTest
{
private const string module = "onnxruntime.dll";
private const string propertiesFile = "Properties.txt";
private readonly ITestOutputHelper output;
public InferenceTest(ITestOutputHelper o)
{
this.output = o;
}
[Fact]
public void TestSessionOptions()
{
using (SessionOptions opt = new SessionOptions())
{
Assert.NotNull(opt);
// check default values of the properties
Assert.Equal(ExecutionMode.ORT_SEQUENTIAL, opt.ExecutionMode);
Assert.True(opt.EnableMemoryPattern);
Assert.False(opt.EnableProfiling);
Assert.Equal("onnxruntime_profile_", opt.ProfileOutputPathPrefix);
Assert.True(opt.EnableCpuMemArena);
Assert.Equal("", opt.LogId);
Assert.Equal(0, opt.LogVerbosityLevel);
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING, opt.LogSeverityLevel);
Assert.Equal(0, opt.IntraOpNumThreads);
Assert.Equal(0, opt.InterOpNumThreads);
Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_ALL, opt.GraphOptimizationLevel);
// try setting options
opt.ExecutionMode = ExecutionMode.ORT_PARALLEL;
Assert.Equal(ExecutionMode.ORT_PARALLEL, opt.ExecutionMode);
opt.EnableMemoryPattern = false;
Assert.False(opt.EnableMemoryPattern);
opt.EnableProfiling = true;
Assert.True(opt.EnableProfiling);
Assert.Equal("onnxruntime_profile_", opt.ProfileOutputPathPrefix);
opt.ProfileOutputPathPrefix = "Ort_P_";
Assert.Equal("Ort_P_", opt.ProfileOutputPathPrefix);
opt.EnableCpuMemArena = false;
Assert.False(opt.EnableCpuMemArena);
opt.LogId = "MyLogId";
Assert.Equal("MyLogId", opt.LogId);
opt.LogVerbosityLevel = 1;
Assert.Equal(1, opt.LogVerbosityLevel);
opt.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR;
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR, opt.LogSeverityLevel);
opt.IntraOpNumThreads = 4;
Assert.Equal(4, opt.IntraOpNumThreads);
opt.InterOpNumThreads = 4;
Assert.Equal(4, opt.InterOpNumThreads);
opt.GraphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_EXTENDED;
Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, opt.GraphOptimizationLevel);
Assert.Throws<OnnxRuntimeException>(() => { opt.GraphOptimizationLevel = (GraphOptimizationLevel)10; });
opt.AppendExecutionProvider_CPU(1);
#if USE_DNNL
opt.AppendExecutionProvider_Dnnl(0);
#endif
#if USE_CUDA
opt.AppendExecutionProvider_CUDA(0);
#endif
#if USE_NGRAPH
opt.AppendExecutionProvider_NGraph("CPU"); //TODO: this API should be refined
#endif
#if USE_OPENVINO
opt.AppendExecutionProvider_OpenVINO();
#endif
#if USE_TENSORRT
opt.AppendExecutionProvider_Tensorrt(0);
#endif
#if USE_MIGRAPHX
opt.AppendExecutionProvider_MIGraphX(0);
#endif
#if USE_NNAPI
opt.AppendExecutionProvider_Nnapi();
#endif
}
}
[Fact]
public void TestRunOptions()
{
using (var opt = new RunOptions())
{
Assert.NotNull(opt);
//verify default options
Assert.False(opt.Terminate);
Assert.Equal(0, opt.LogVerbosityLevel);
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING, opt.LogSeverityLevel);
Assert.Equal("", opt.LogId);
// try setting options
opt.Terminate = true;
Assert.True(opt.Terminate);
opt.LogVerbosityLevel = 1;
Assert.Equal(1, opt.LogVerbosityLevel);
opt.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR;
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR, opt.LogSeverityLevel);
opt.LogId = "MyLogTag";
Assert.Equal("MyLogTag", opt.LogId);
}
}
[Fact]
public void CanCreateAndDisposeSessionWithModelPath()
{
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
using (var session = new InferenceSession(modelPath))
{
Assert.NotNull(session);
Assert.NotNull(session.InputMetadata);
Assert.Equal(1, session.InputMetadata.Count); // 1 input node
Assert.True(session.InputMetadata.ContainsKey("data_0")); // input node name
Assert.Equal(typeof(float), session.InputMetadata["data_0"].ElementType);
Assert.True(session.InputMetadata["data_0"].IsTensor);
var expectedInputDimensions = new int[] { 1, 3, 224, 224 };
Assert.Equal(expectedInputDimensions.Length, session.InputMetadata["data_0"].Dimensions.Length);
for (int i = 0; i < expectedInputDimensions.Length; i++)
{
Assert.Equal(expectedInputDimensions[i], session.InputMetadata["data_0"].Dimensions[i]);
}
Assert.NotNull(session.OutputMetadata);
Assert.Equal(1, session.OutputMetadata.Count); // 1 output node
Assert.True(session.OutputMetadata.ContainsKey("softmaxout_1")); // output node name
Assert.Equal(typeof(float), session.OutputMetadata["softmaxout_1"].ElementType);
Assert.True(session.OutputMetadata["softmaxout_1"].IsTensor);
var expectedOutputDimensions = new int[] { 1, 1000, 1, 1 };
Assert.Equal(expectedOutputDimensions.Length, session.OutputMetadata["softmaxout_1"].Dimensions.Length);
for (int i = 0; i < expectedOutputDimensions.Length; i++)
{
Assert.Equal(expectedOutputDimensions[i], session.OutputMetadata["softmaxout_1"].Dimensions[i]);
}
}
}
[Theory]
[InlineData(GraphOptimizationLevel.ORT_DISABLE_ALL, true)]
[InlineData(GraphOptimizationLevel.ORT_DISABLE_ALL, false)]
[InlineData(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, true)]
[InlineData(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, false)]
private void CanRunInferenceOnAModel(GraphOptimizationLevel graphOptimizationLevel, bool enableParallelExecution)
{
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
// Set the graph optimization level for this session.
SessionOptions options = new SessionOptions();
options.GraphOptimizationLevel = graphOptimizationLevel;
if (enableParallelExecution) options.ExecutionMode = ExecutionMode.ORT_PARALLEL;
using (var session = new InferenceSession(modelPath, options))
{
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
float[] inputData = LoadTensorFromFile(@"bench.in"); // this is the data for only one input tensor for this model
foreach (var name in inputMeta.Keys)
{
Assert.Equal(typeof(float), inputMeta[name].ElementType);
Assert.True(inputMeta[name].IsTensor);
var tensor = new DenseTensor<float>(inputData, inputMeta[name].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
}
ReadOnlySpan<int> expectedOutputDimensions = new int[] { 1, 1000, 1, 1 };
string[] expectedOutputNames = new string[] { "softmaxout_1" };
// Run inference with named inputs and outputs created with in Run()
using (var results = session.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
validateRunResults(results);
}
// Run inference with named inputs, outputs created with in Run() and RunOptions
using (var runOptions = new RunOptions())
{
runOptions.LogId = "CsharpTest";
runOptions.Terminate = false; // TODO: Test terminate = true, it currently crashes
runOptions.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR;
IReadOnlyCollection<string> outputNames = session.OutputMetadata.Keys.ToList();
using (var results = session.Run(container, outputNames, runOptions)) // results is an IReadOnlyList<NamedOnnxValue> container
{
validateRunResults(results);
}
}
// Run inference with pinned inputs and outputs created with in Run()
using (var pinnedInputs = new DisposableList<FixedBufferOnnxValue>())
{
var inputNames = container.Select(i => i.Name).ToArray();
pinnedInputs.AddRange(container.Select(i => FixedBufferOnnxValue.CreateFromTensor(i.AsTensor<float>())));
// output names not specified
using (var results = session.Run(inputNames, pinnedInputs)) // results is an IReadOnlyList<NamedOnnxValue> container
{
validateRunResults(results);
}
// output names specified explicitly
using (var results = session.Run(inputNames, pinnedInputs, expectedOutputNames)) // results is an IReadOnlyList<NamedOnnxValue> container
{
validateRunResults(results);
}
}
// Run inference with named inputs and named outputs
{
// correct pre-allocated outputs
var expectedOutputValues = new List<NamedOnnxValue>()
{
NamedOnnxValue.CreateFromTensor("softmaxout_1", new DenseTensor<float>(expectedOutputDimensions))
};
session.Run(container, expectedOutputValues);
validateRunResultData(expectedOutputValues[0].AsTensor<float>());
}
// Run inference with pinned inputs and named outputs
using (var pinnedInputs = new DisposableList<FixedBufferOnnxValue>())
{
var inputNames = container.Select(i => i.Name).ToArray();
pinnedInputs.AddRange(container.Select(i => FixedBufferOnnxValue.CreateFromTensor(i.AsTensor<float>())));
// expected inputs and outputs
var expectedOutputValues = new List<NamedOnnxValue>()
{
NamedOnnxValue.CreateFromTensor("softmaxout_1", new DenseTensor<float>(expectedOutputDimensions))
};
session.Run(inputNames, pinnedInputs, expectedOutputValues);
validateRunResultData(expectedOutputValues[0].AsTensor<float>());
}
// Run inference with named inputs and pinned outputs
{
// correct pre-allocated outputs
using (var pinnedOutputs = new DisposableList<FixedBufferOnnxValue>())
{
var outputTensor = new DenseTensor<float>(expectedOutputDimensions);
pinnedOutputs.Add(FixedBufferOnnxValue.CreateFromTensor(outputTensor));
session.Run(container, expectedOutputNames, pinnedOutputs);
validateRunResultData(outputTensor);
}
}
// Run inference with pinned inputs and pinned outputs
using (DisposableList<FixedBufferOnnxValue> pinnedInputs = new DisposableList<FixedBufferOnnxValue>(),
pinnedOutputs = new DisposableList<FixedBufferOnnxValue>())
{
var inputNames = container.Select(i => i.Name).ToArray();
pinnedInputs.AddRange(container.Select(i => FixedBufferOnnxValue.CreateFromTensor(i.AsTensor<float>())));
var outputTensor = new DenseTensor<float>(expectedOutputDimensions);
pinnedOutputs.Add(FixedBufferOnnxValue.CreateFromTensor(outputTensor));
session.Run(inputNames, pinnedInputs, expectedOutputNames, pinnedOutputs);
validateRunResultData(outputTensor);
}
}
}
[Fact]
public void InferenceSessionManualDisposeAfterUse()
{
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
// Set the graph optimization level for this session.
SessionOptions options = new SessionOptions();
options.ProfileOutputPathPrefix = "Ort_P_";
options.EnableProfiling = true;
var session = new InferenceSession(modelPath, options);
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
float[] inputData = LoadTensorFromFile(@"bench.in"); // this is the data for only one input tensor for this model
foreach (var name in inputMeta.Keys)
{
Assert.Equal(typeof(float), inputMeta[name].ElementType);
Assert.True(inputMeta[name].IsTensor);
var tensor = new DenseTensor<float>(inputData, inputMeta[name].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
}
// Run inference with named inputs and outputs created with in Run()
using (var results = session.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
validateRunResults(results);
}
string profile_file = session.EndProfiling();
// Profile file should have the output path prefix in it
Assert.Contains("Ort_P_", profile_file);
// Should be able to dispose the session manually
session.Dispose();
}
private void validateRunResults(IReadOnlyCollection<NamedOnnxValue> results)
{
// validate the results
foreach (var r in results)
{
Assert.Equal(1, results.Count);
Assert.Equal("softmaxout_1", r.Name);
validateRunResultData(r.AsTensor<float>());
}
}
private void validateRunResultData(Tensor<float> resultTensor)
{
float[] expectedOutput = LoadTensorFromFile(@"bench.expected_out");
int[] expectedDimensions = { 1, 1000, 1, 1 }; // hardcoded for now for the test data
Assert.Equal(expectedDimensions.Length, resultTensor.Rank);
var resultDimensions = resultTensor.Dimensions;
for (int i = 0; i < expectedDimensions.Length; i++)
{
Assert.Equal(expectedDimensions[i], resultDimensions[i]);
}
var resultArray = resultTensor.ToArray();
Assert.Equal(expectedOutput.Length, resultArray.Length);
Assert.Equal(expectedOutput, resultArray, new floatComparer());
}
[Fact]
private void ThrowWrongInputName()
{
var tuple = OpenSessionSqueezeNet();
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>("wrong_name", tensor));
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
Assert.Contains("Invalid Feed Input", ex.Message);
session.Dispose();
}
[Fact]
private void ThrowWrongInputType()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
int[] inputDataInt = inputData.Select(x => (int)x).ToArray();
var tensor = new DenseTensor<int>(inputDataInt, inputMeta["data_0"].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<int>("data_0", tensor));
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
var msg = ex.ToString().Substring(0, 101);
// TODO: message is diff in LInux. Use substring match
Assert.Equal("Microsoft.ML.OnnxRuntime.OnnxRuntimeException: [ErrorCode:InvalidArgument] Unexpected input data type", msg);
session.Dispose();
}
[Fact]
private void ThrowExtraInputs()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var tensor = tuple.Item3;
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
var nov1 = NamedOnnxValue.CreateFromTensor<float>("data_0", tensor);
var nov2 = NamedOnnxValue.CreateFromTensor<float>("extra", tensor);
container.Add(nov1);
container.Add(nov2);
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
Assert.StartsWith("[ErrorCode:InvalidArgument] Invalid Feed Input Name", ex.Message);
session.Dispose();
}
[Fact]
private void ThrowInconsistentPinnedInputs()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var tensor = tuple.Item3;
using (var inputs = new DisposableList<FixedBufferOnnxValue>())
{
inputs.Add(FixedBufferOnnxValue.CreateFromTensor(tensor));
var ex = Assert.Throws<ArgumentException>(() => session.Run(new string[0], inputs));
Assert.StartsWith("Length of inputNames (0) must match that of inputValues (1).", ex.Message);
}
}
[Fact]
private void ThrowWrongOutputName()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 2 });
var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("bad_output_name", outputTensor) };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, outputs));
Assert.Contains("Invalid Output Name", ex.Message);
session.Dispose();
}
[Fact]
private void ThrowWrongOutputType()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<int>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, outputs));
// TODO: check exception message
// InferenceSession::ValidateOutputs() does not check type so far. Currently this will finally trigger an error in Softmax.
session.Dispose();
}
[Fact]
private void ThrowWrongOutputDimension()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1001, 1, 1 });
var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, outputs));
// TODO: check exception message
// InferenceSession::ValidateOutputs() does not check dims so far. Currently this will finally trigger an error in Softmax.
session.Dispose();
}
[Fact]
private void ThrowNoOutput()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, new NamedOnnxValue[0]));
Assert.Contains("[ErrorCode:InvalidArgument] At least one output should be requested.", ex.Message);
session.Dispose();
}
[Fact]
private void ThrowInconsistentPinnedOutputs()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
using (var outputs = new DisposableList<FixedBufferOnnxValue>())
{
var ex = Assert.Throws<ArgumentException>(() => session.Run(inputs, new string[] { "softmaxout_1" }, outputs));
Assert.StartsWith("Length of outputNames (1) must match that of outputValues (0).", ex.Message);
}
}
[Fact]
private void TestMultiThreads()
{
var numThreads = 10;
var loop = 10;
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var tensor = tuple.Item3;
var expectedOut = tuple.Item4;
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
container.Add(NamedOnnxValue.CreateFromTensor<float>("data_0", tensor));
var tasks = new Task[numThreads];
for (int i = 0; i < numThreads; i++)
{
tasks[i] = Task.Factory.StartNew(() =>
{
for (int j = 0; j < loop; j++)
{
var resnov = session.Run(container);
var res = resnov.ToArray()[0].AsTensor<float>().ToArray<float>();
Assert.Equal(res, expectedOut, new floatComparer());
}
});
};
Task.WaitAll(tasks);
session.Dispose();
}
private static Dictionary<string, string> GetSkippedModels()
{
var skipModels = new Dictionary<string, string>() {
{ "mxnet_arcface", "Model is an invalid ONNX model"},
{ "tf_inception_v2", "TODO: Debug failing model, skipping for now" },
{ "fp16_inception_v1", "16-bit float not supported type in C#." },
{ "fp16_shufflenet", "16-bit float not supported type in C#." },
{ "fp16_tiny_yolov2", "16-bit float not supported type in C#." },
{ "BERT_Squad", "Could not find an implementation for the node bert / embeddings / one_hot:OneHot(9)" },
{ "mlperf_ssd_mobilenet_300", "Could not find file output_0.pb" },
{ "tf_resnet_v1_50", "result mismatch when Conv BN Fusion is applied" },
{ "tf_resnet_v1_101", "result mismatch when Conv BN Fusion is applied" },
{ "tf_resnet_v1_152", "result mismatch when Conv BN Fusion is applied" },
{ "coreml_Imputer-LogisticRegression_sklearn_load_breast_cancer", "Can't determine model file name" },
{ "mask_rcnn_keras", "Model should be edited to remove the extra outputs" },
};
// The following models fails on nocontribops win CI
var disableContribOpsEnvVar = Environment.GetEnvironmentVariable("DisableContribOps");
var isContribOpsDisabled = (disableContribOpsEnvVar != null) ? disableContribOpsEnvVar.Equals("ON") : false;
if (isContribOpsDisabled)
{
skipModels["test_tiny_yolov2"] = "Fails when ContribOps is disabled";
skipModels["mask_rcnn_keras"] = "Pad is not a registered function/op";
}
// This model fails on x86 Win CI
if (System.Environment.Is64BitProcess == false)
{
skipModels["test_vgg19"] = "Get preallocated buffer for initializer conv4_4_b_0 failed";
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";
skipModels["tf_nasnet_large"] = "Get preallocated buffer for initializer ConvBnFusion_BN_B_cell_11/beginning_bn/beta:0_331 failed";
skipModels["test_zfnet512"] = "System out of memory";
skipModels["test_bvlc_reference_caffenet"] = "System out of memory";
}
return skipModels;
}
public static IEnumerable<object[]> GetModelsForTest()
{
var modelsDir = GetTestModelsDir();
var modelsDirInfo = new DirectoryInfo(modelsDir);
var skipModels = GetSkippedModels();
foreach (var opsetDir in modelsDirInfo.EnumerateDirectories())
{
//var modelRoot = new DirectoryInfo(Path.Combine(modelsDir, opsetDir.Name));
foreach (var modelDir in opsetDir.EnumerateDirectories())
{
if (!skipModels.ContainsKey(modelDir.Name))
{
yield return new object[] { modelDir.Parent.Name, modelDir.Name };
}
} //model
} //opset
}
public static IEnumerable<object[]> GetSkippedModelForTest()
{
var modelsDir = GetTestModelsDir();
var modelsDirInfo = new DirectoryInfo(modelsDir);
var skipModels = GetSkippedModels();
foreach (var opsetDir in modelsDirInfo.EnumerateDirectories())
{
var modelRoot = new DirectoryInfo(Path.Combine(modelsDir, opsetDir.Name));
foreach (var modelDir in modelRoot.EnumerateDirectories())
{
if (skipModels.ContainsKey(modelDir.Name))
{
//Console.WriteLine("Model {0} is skipped due to the error: {1}", modelDir.FullName, skipModels[modelDir.Name]);
yield return new object[] { modelDir.Parent.Name, modelDir.Name };
}
}
}
}
[Theory]
[MemberData(nameof(GetModelsForTest))]
[MemberData(nameof(GetSkippedModelForTest), Skip = "Skipped due to Error, please fix the error and enable the test")]
private void TestPreTrainedModels(string opset, string modelName)
{
var modelsDir = GetTestModelsDir();
string onnxModelFileName = null;
var modelDir = new DirectoryInfo(Path.Combine(modelsDir, opset, modelName));
try
{
var onnxModelNames = modelDir.GetFiles("*.onnx");
bool validModelFound = false;
if (onnxModelNames.Length > 0)
{
// TODO remove file "._resnet34v2.onnx" from test set
for (int i = 0; i < onnxModelNames.Length; i++)
{
if (onnxModelNames[i].Name != "._resnet34v2.onnx")
{
onnxModelNames[0] = onnxModelNames[i];
validModelFound = true;
}
}
}
if (validModelFound)
{
onnxModelFileName = Path.Combine(modelDir.FullName, onnxModelNames[0].Name);
}
else
{
var modelNamesList = string.Join(",", onnxModelNames.Select(x => x.ToString()));
throw new Exception($"Opset {opset} Model {modelName}. Can't determine model file name. Found these :{modelNamesList}");
}
using (var session = new InferenceSession(onnxModelFileName))
{
var inMeta = session.InputMetadata;
string testDataDirNamePattern = "test_data*";
if (opset == "opset9" && modelName == "LSTM_Seq_lens_unpacked")
{
testDataDirNamePattern = "seq_lens*"; // discrepency in data directory
}
foreach (var testDataDir in modelDir.EnumerateDirectories(testDataDirNamePattern))
{
var inputContainer = new List<NamedOnnxValue>();
var outputContainer = new List<NamedOnnxValue>();
foreach (var f in testDataDir.EnumerateFiles("input_*.pb"))
{
inputContainer.Add(LoadTensorFromFilePb(f.FullName, inMeta));
}
foreach (var f in testDataDir.EnumerateFiles("output_*.pb"))
{
outputContainer.Add(LoadTensorFromFilePb(f.FullName, session.OutputMetadata));
}
using (var resultCollection = session.Run(inputContainer))
{
foreach (var result in resultCollection)
{
Assert.True(session.OutputMetadata.ContainsKey(result.Name));
var outputMeta = session.OutputMetadata[result.Name];
NamedOnnxValue outputValue = null;
foreach (var o in outputContainer)
{
if (o.Name == result.Name)
{
outputValue = o;
break;
}
}
if (outputValue == null)
{
outputValue = outputContainer.First(); // in case the output data file does not contain the name
}
if (outputMeta.IsTensor)
{
if (outputMeta.ElementType == typeof(float))
{
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 TestReusingRunOutputNonStringType()
{
// 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);
var res1 = session.Run(container);
// change the name of the DisposableNamedOnnxValue
res1.First().Name = "input";
// Run inferencing 2 times using the output of the first Run()
for (int i = 0; i < 2; ++i)
{
using (var res2 = session.Run(res1))
{
var tensorOut = res2.First().AsTensor<bool>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
}
[Fact]
private void TestReusingRunOutputStringType()
{
// 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[] { "a", "b", "c", "d", "e" }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res1 = session.Run(container);