mirror of
https://github.com/saymrwulf/onnxruntime.git
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31e7d3d7d4
### Description <!-- Describe your changes. --> Disable new test that is failing on linux. Not required for this release. Will fix in the next week. Marshal.Prelink can be used on Windows to make the symbol available but Linux appears to work differently. Also need to update the pre-checkin tests so this is tested early as it's only failing in the E2E tests run in the packaging pipeline. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Fix packaging pipeline error.
1092 lines
54 KiB
C#
1092 lines
54 KiB
C#
using System;
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using System.Collections.Generic;
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using System.IO;
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using System.Linq;
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using System.Runtime.InteropServices;
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using System.Text.RegularExpressions;
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using Microsoft.ML.OnnxRuntime.Tensors;
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using Xunit;
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namespace Microsoft.ML.OnnxRuntime.Tests
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{
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/// <summary>
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/// This is compensate for the absence of string.Contains() in .NET Standard 2.0
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/// Contains(String, StringComparison)
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/// </summary>
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public static class StringExtensions
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{
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public static bool Contains(this String str, String substring,
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StringComparison comp)
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{
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if (substring == null)
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throw new ArgumentNullException("substring",
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"substring cannot be null.");
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else if (!Enum.IsDefined(typeof(StringComparison), comp))
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throw new ArgumentException("comp is not a member of StringComparison",
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"comp");
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return str.IndexOf(substring, comp) >= 0;
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}
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}
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public partial 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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[Fact(DisplayName = "CanCreateAndDisposeSessionWithModelPath")]
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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 nodeMeta
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Assert.True(session.InputMetadata.ContainsKey("data_0")); // input nodeMeta 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 nodeMeta
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Assert.True(session.OutputMetadata.ContainsKey("softmaxout_1")); // output nodeMeta 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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#if USE_CUDA
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[Fact(DisplayName = "TestCUDAProviderOptions")]
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private void TestCUDAProviderOptions()
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{
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string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
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using (var cleanUp = new DisposableListTest<IDisposable>())
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{
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var cudaProviderOptions = new OrtCUDAProviderOptions();
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cleanUp.Add(cudaProviderOptions);
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var providerOptionsDict = new Dictionary<string, string>();
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providerOptionsDict["device_id"] = "0";
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// 256MB
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providerOptionsDict["gpu_mem_limit"] = "268435456";
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providerOptionsDict["arena_extend_strategy"] = "kSameAsRequested";
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providerOptionsDict["cudnn_conv_algo_search"] = "DEFAULT";
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providerOptionsDict["do_copy_in_default_stream"] = "1";
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providerOptionsDict["cudnn_conv_use_max_workspace"] = "1";
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providerOptionsDict["cudnn_conv1d_pad_to_nc1d"] = "1";
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cudaProviderOptions.UpdateOptions(providerOptionsDict);
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var resultProviderOptionsDict = new Dictionary<string, string>();
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ProviderOptionsValueHelper.StringToDict(cudaProviderOptions.GetOptions(), resultProviderOptionsDict);
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// test provider options configuration
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string value;
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value = resultProviderOptionsDict["device_id"];
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Assert.Equal("0", value);
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value = resultProviderOptionsDict["gpu_mem_limit"];
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Assert.Equal("268435456", value);
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value = resultProviderOptionsDict["arena_extend_strategy"];
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Assert.Equal("kSameAsRequested", value);
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value = resultProviderOptionsDict["cudnn_conv_algo_search"];
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Assert.Equal("DEFAULT", value);
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value = resultProviderOptionsDict["do_copy_in_default_stream"];
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Assert.Equal("1", value);
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value = resultProviderOptionsDict["cudnn_conv_use_max_workspace"];
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Assert.Equal("1", value);
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value = resultProviderOptionsDict["cudnn_conv1d_pad_to_nc1d"];
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Assert.Equal("1", value);
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// test correctness of provider options
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SessionOptions options = SessionOptions.MakeSessionOptionWithCudaProvider(cudaProviderOptions);
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cleanUp.Add(options);
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var session = new InferenceSession(modelPath, options);
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cleanUp.Add(session);
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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float[] inputData = TestDataLoader.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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session.Run(container);
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}
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}
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#endif
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#if USE_TENSORRT
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[Fact(DisplayName = "CanRunInferenceOnAModelWithTensorRT")]
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private void CanRunInferenceOnAModelWithTensorRT()
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{
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string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
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using (var cleanUp = new DisposableListTest<IDisposable>())
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{
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SessionOptions options = SessionOptions.MakeSessionOptionWithTensorrtProvider(0);
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cleanUp.Add(options);
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var session = new InferenceSession(modelPath, options);
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cleanUp.Add(session);
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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float[] inputData = TestDataLoader.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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using (var results = session.Run(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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[Fact(DisplayName = "TestTensorRTProviderOptions")]
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private void TestTensorRTProviderOptions()
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{
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string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
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string calTablePath = "squeezenet_calibration.flatbuffers";
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string enginePath = "./";
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string engineDecrptLibPath = "engine_decryp";
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using (var cleanUp = new DisposableListTest<IDisposable>())
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{
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var trtProviderOptions = new OrtTensorRTProviderOptions();
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cleanUp.Add(trtProviderOptions);
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var providerOptionsDict = new Dictionary<string, string>();
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providerOptionsDict["device_id"] = "0";
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providerOptionsDict["trt_fp16_enable"] = "1";
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providerOptionsDict["trt_int8_enable"] = "1";
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providerOptionsDict["trt_int8_calibration_table_name"] = calTablePath;
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providerOptionsDict["trt_engine_cache_enable"] = "1";
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providerOptionsDict["trt_engine_cache_path"] = enginePath;
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providerOptionsDict["trt_engine_decryption_enable"] = "0";
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providerOptionsDict["trt_engine_decryption_lib_path"] = engineDecrptLibPath;
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trtProviderOptions.UpdateOptions(providerOptionsDict);
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var resultProviderOptionsDict = new Dictionary<string, string>();
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ProviderOptionsValueHelper.StringToDict(trtProviderOptions.GetOptions(), resultProviderOptionsDict);
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// test provider options configuration
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string value;
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value = resultProviderOptionsDict["device_id"];
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Assert.Equal("0", value);
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value = resultProviderOptionsDict["trt_fp16_enable"];
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Assert.Equal("1", value);
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value = resultProviderOptionsDict["trt_int8_enable"];
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Assert.Equal("1", value);
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value = resultProviderOptionsDict["trt_int8_calibration_table_name"];
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Assert.Equal(calTablePath, value);
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value = resultProviderOptionsDict["trt_engine_cache_enable"];
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Assert.Equal("1", value);
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value = resultProviderOptionsDict["trt_engine_cache_path"];
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Assert.Equal(enginePath, value);
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value = resultProviderOptionsDict["trt_engine_decryption_enable"];
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Assert.Equal("0", value);
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value = resultProviderOptionsDict["trt_engine_decryption_lib_path"];
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Assert.Equal(engineDecrptLibPath, value);
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// test correctness of provider options
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SessionOptions options = SessionOptions.MakeSessionOptionWithTensorrtProvider(trtProviderOptions);
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cleanUp.Add(options);
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var session = new InferenceSession(modelPath, options);
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cleanUp.Add(session);
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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float[] inputData = TestDataLoader.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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session.Run(container);
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}
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}
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#endif
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private static Func<DirectoryInfo, IEnumerable<DirectoryInfo>> getOpsetDirectories = delegate (DirectoryInfo modelsDirInfo)
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{
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return modelsDirInfo.EnumerateDirectories("opset*", SearchOption.AllDirectories);
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};
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private static Dictionary<string, string> GetSkippedModels(DirectoryInfo modelsDirInfo)
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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_tiny_yolov2", "Tolerance level for float16 is not known. We now support fp16." },
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{ "fp16_test_tiny_yolov2", "ImageScaler is not a registered function/op"},
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{ "fp16_coreml_FNS-Candy", "ImageScaler is not a registered function/op" },
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{ "fp16_coreml_LinearRegression_NYCTaxi", "Error in Node:featureVectorizer : No Op registered for FeatureVectorizer with domain_version of 1"},
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{ "test_mnist", "Does not run in opset9, runs in other opsets. The model runs but I don't have a data set to debug output locally. Tensors of type ElementType not currently supported in the LoadTensorFromFile" },
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{ "BERT_Squad", "Could not find an implementation for the nodeMeta 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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{ "cntk_simple_seg", "Bad onnx test output caused by wrong SAME_UPPER/SAME_LOWER for ConvTranspose" },
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{ "coreml_Imputer-LogisticRegression_sklearn_load_breast_cancer", "Can't determine model file name" },
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{ "mask_rcnn_keras", "Model should be edited to remove the extra outputs" },
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{ "test_maxunpool_export_with_output_shape", "results mismatch"},
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{ "test_min_int8", "Could not find an implementation for Min(13) node with name"},
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{ "test_min_uint8", "Could not find an implementation for Min(13) node with name"},
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{ "test_min_int16", "Could not find an implementation for Min(13) node with name"},
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{ "test_min_uint16", "Could not find an implementation for Min(13) node with name"},
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{ "test_max_int8", "Could not find an implementation for Max(13) node with name"},
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{ "test_max_uint8", "Could not find an implementation for Max(13) node with name"},
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{ "test_max_int16", "Could not find an implementation for Max(13) node with name"},
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{ "test_max_uint16", "Could not find an implementation for Max(13) nodeMeta with name '"},
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{ "test_mul_uint8", "Could not find an implementation for Mul(14) node with name" },
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{ "test_cast_STRING_to_FLOAT", "Output mismatch"},
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{ "test_cast_BFLOAT16_to_FLOAT", "Output mismatch"},
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{ "test_cast_FLOAT_to_STRING", "Output strings can not be compared exactly"},
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{ "test_castlike_STRING_to_FLOAT", "Output mismatch"},
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{ "test_castlike_STRING_to_FLOAT_expanded", "Output mismatch"},
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{ "test_castlike_BFLOAT16_to_FLOAT", "Length is expected to be equal to Count (metadata and expected data mismatch) "},
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{ "test_castlike_BFLOAT16_to_FLOAT_expanded", "Length is expected to be equal to Count metadata and expected data mismatch"},
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{ "test_castlike_FLOAT_to_BFLOAT16", "Length is expected to be equal to Count. Testdata dims length do not match that of model metadata"},
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{ "test_castlike_FLOAT_to_BFLOAT16_expanded", "Length is expected to be equal to Count"},
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{ "test_castlike_FLOAT_to_STRING", "string comparison does not match due to float rounding"},
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{ "test_castlike_FLOAT_to_STRING_expanded", "string comparison does not match due to float rounding"},
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{ "test_bitshift_right_uint16", "Could not find an implementation for BitShift(11) nodeMeta with name ''"},
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{ "test_bitshift_left_uint16", "Could not find an implementation for BitShift(11)"},
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{ "test_pow_types_float32_uint64", "Could not find an implementation for Pow(15) node with name ''"},
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{ "test_pow_types_float32_uint32", "Could not find an implementation for Pow(15) node with name ''"},
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{ "test_resize_downsample_scales_cubic_align_corners", "Results mismatch"},
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{ "test_resize_downsample_scales_linear_align_corners", "Results mismatch"},
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{ "test_gru_batchwise", "batchwise operations not supported"},
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{ "test_lstm_batchwise", "Batchwise recurrent operations(layout == 1) are not supported.If you need support create a github issue with justification."},
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{ "test_simple_rnn_batchwise", "batchwise operations not supported"},
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{ "test_batchnorm_example_training_mode", "opset14 version not implemented yet"},
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{ "test_bernoulli", "random generator, results mismatch"},
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{ "test_bernoulli_seed", "random generator, results mismatch"},
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{ "test_bernoulli_double", "random generator, results mismatch"},
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{ "test_bernoulli_expanded", "random generator, results mismatch"},
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{ "test_bernoulli_seed_expanded", "random generator, results mismatch"},
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{ "test_bernoulli_double_expanded", "random generator, results mismatch"},
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// the expansion of Softplus uses Exp(1). ORT has a Softplus kernel, so testing the expansion is
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// unnecessary and fails as ORT support for Exp started at opset 6 (as ORT didn't exist until opset 7).
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{ "test_clip_default_int8_max_expanded", "Could not find an implementation for Less(13) nodeMeta with name ''" },
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{ "test_softplus_expanded", "Could not find an implementation for Exp(1) node with name ''"},
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{ "test_softplus_example_expanded", "Could not find an implementation for Exp(1) node with name ''"},
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{ "test_div_uint8", "Could not find an implementation for Div(14) nodeMeta with name ''"},
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{ "test_add_uint8", "Opset18 Could not find an implementation for Add(14) nodeMeta with name ''"},
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{ "test_col2im_pads", "Results mismatch due to a typo in test data"},
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{ "test_optional_has_element_empty_optional_input", "OptionalProto test metadata. Unable to load 'optional_input' optional element type of: Undefined type"},
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{ "test_loop13_seq", "3rd input is an empty sequence. Ort API does not tolerate empty seq: Number of values should be at least 1" },
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// Training tests
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{ "BERT-Squad-int8", "training domain"},
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{ "YOLOv3-12-int8", "training_domain"},
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{ "test_training_dropout_default", "results mismatch"},
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{ "test_training_dropout_default_mask", "Results mismatch"},
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{ "test_training_dropout", "results mismatch"},
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{ "test_training_dropout_mask", "results mismatch."},
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{ "test_momentum", "ai.onnx.preview.training:Momentum(-1) is not a registered function/op"},
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{ "test_momentum_multiple", "ai.onnx.preview.training:Momentum(-1) is not a registered function/op"},
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{ "test_nesterov_momentum", "ai.onnx.preview.training:Momentum(-1) is not a registered function/op"},
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{ "test_adam", "ai.onnx.preview.training:Adam(-1) is not a registered function/op"},
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{ "test_adam_multiple", "ai.onnx.preview.training:Adam(-1) is not a registered function/op"},
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{ "test_adagrad", "ai.onnx.preview.training:Adagrad(-1) is not a registered function/op"},
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{ "test_adagrad_multiple", "ai.onnx.preview.training:Adagrad(-1) is not a registered function/op"},
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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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// Skip traditional ML models
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var disableMlOpsEnvVar = Environment.GetEnvironmentVariable("DisableMlOps");
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var isMlOpsDisabled = (disableMlOpsEnvVar != null) ? disableMlOpsEnvVar.Equals("ON") : false;
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if (isMlOpsDisabled)
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{
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foreach (var opsetDir in getOpsetDirectories(modelsDirInfo))
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{
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foreach (var modelDir in opsetDir.EnumerateDirectories())
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{
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var modelDirName = modelDir.Name;
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if (modelDirName.StartsWith("scikit_") ||
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modelDirName.StartsWith("libsvm_") ||
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modelDirName.StartsWith("coreml_") ||
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modelDirName.StartsWith("keras2coreml_") ||
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modelDirName.StartsWith("XGBoost_"))
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{
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skipModels[modelDirName] = "Fails when ML ops are disabled";
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}
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} //model
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} //opset
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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["GPT2_LM_HEAD"] = "System out of memory";
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skipModels["GPT2"] = "System out of memory";
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skipModels["test_GPT2"] = "System out of memory";
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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["ZFNet-512"] = "System out of memory";
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skipModels["test_bvlc_reference_caffenet"] = "System out of memory";
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skipModels["coreml_VGG16_ImageNet"] = "System out of memory";
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skipModels["test_ssd"] = "System out of memory";
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skipModels["roberta_sequence_classification"] = "System out of memory";
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// models from model zoo
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|
skipModels["VGG 19"] = "bad allocation";
|
|
skipModels["VGG 19-caffe2"] = "bad allocation";
|
|
skipModels["VGG 19-bn"] = "bad allocation";
|
|
skipModels["VGG 16"] = "bad allocation";
|
|
skipModels["VGG 16-bn"] = "bad allocation";
|
|
skipModels["VGG 16-fp32"] = "bad allocation";
|
|
}
|
|
|
|
return skipModels;
|
|
}
|
|
|
|
public static IEnumerable<object[]> GetModelsForTest()
|
|
{
|
|
var modelsDir = GetTestModelsDir();
|
|
var modelsDirInfo = new DirectoryInfo(modelsDir);
|
|
var skipModels = GetSkippedModels(modelsDirInfo);
|
|
|
|
foreach (var opsetDir in getOpsetDirectories(modelsDirInfo))
|
|
{
|
|
//var modelRoot = new DirectoryInfo(Path.Combine(modelsDir, opsetDir.Name));
|
|
foreach (var modelDir in opsetDir.EnumerateDirectories())
|
|
{
|
|
if (!(skipModels.ContainsKey(modelDir.Name) ||
|
|
modelDir.Name.Contains("int8", StringComparison.OrdinalIgnoreCase) ||
|
|
modelDir.Name.Contains("qdq", StringComparison.OrdinalIgnoreCase)))
|
|
{
|
|
yield return new object[] { modelDir.Parent.FullName, modelDir.Name };
|
|
}
|
|
} //model
|
|
} //opset
|
|
}
|
|
|
|
public static IEnumerable<object[]> GetSkippedModelForTest()
|
|
{
|
|
var modelsDir = GetTestModelsDir();
|
|
var modelsDirInfo = new DirectoryInfo(modelsDir);
|
|
var skipModels = GetSkippedModels(modelsDirInfo);
|
|
|
|
foreach (var opsetDir in getOpsetDirectories(modelsDirInfo))
|
|
{
|
|
foreach (var modelDir in opsetDir.EnumerateDirectories())
|
|
{
|
|
if (skipModels.ContainsKey(modelDir.Name) ||
|
|
modelDir.Name.Contains("int8", StringComparison.OrdinalIgnoreCase) ||
|
|
modelDir.Name.Contains("qdq", StringComparison.OrdinalIgnoreCase))
|
|
{
|
|
//Console.WriteLine("Model {0} is skipped due to the error: {1}", modelDir.FullName, skipModels[modelDir.Name]);
|
|
yield return new object[] { modelDir.Parent.FullName, modelDir.Name };
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
private string MatchInputOutputWithFile(string fileName, InferenceSession session, bool input, out NodeMetadata result)
|
|
{
|
|
string nodeName = string.Empty;
|
|
result = null;
|
|
var names = (input) ? session.InputNames : session.OutputNames;
|
|
var metadata = (input) ? session.InputMetadata : session.OutputMetadata;
|
|
string regEx = (input) ? @"input_(\d{1,}).pb" : @"output_(\d{1,}).pb";
|
|
var inpOut = (input) ? "input" : "output";
|
|
|
|
// Extract the number from the file name, if not try to match the input/output name with the name of the file.
|
|
try
|
|
{
|
|
// captures start at index 1
|
|
var group = Regex.Matches(fileName, regEx).Single().Groups[1];
|
|
var num = int.Parse(group.Value);
|
|
if (num >= 0 && num < names.Count)
|
|
{
|
|
nodeName = names[num];
|
|
result = metadata[nodeName];
|
|
}
|
|
else
|
|
{
|
|
throw new InvalidDataException($"Filename '{fileName}' {inpOut} number '{num}' is out of range for '{names.Count}' {inpOut}(s)");
|
|
}
|
|
}
|
|
catch (Exception)
|
|
{
|
|
// Either does not match or can not parse the number
|
|
}
|
|
|
|
if (result is null)
|
|
{
|
|
throw new InvalidDataException($"Unable to match file: {fileName} to input/output metadata");
|
|
}
|
|
return nodeName;
|
|
}
|
|
|
|
// The numbering of the input files does not match the order of outputs
|
|
// listed in the metadata of test_BERT_Squad. Model metadata order:
|
|
// "unique_ids_raw_output___9:0", "segment_ids:0", "input_mask:0", "input_ids:0"
|
|
// The corr input files are: input_0.pb, input_3.pb, input_2.pb, input_1.pb
|
|
// Everything in reverse, but the 0.
|
|
|
|
// Previously, it worked because our test data has matching
|
|
// tensor names that we could match to metadata after we load the tensor.
|
|
// But now, we need to know ahead of time what Onnx type we load, and thus match
|
|
// metadata with the test data file before loading. Protobuf can happily load whatever
|
|
// and give you garbage.
|
|
|
|
private string MatchBertSquadInputs(string fileName)
|
|
{
|
|
string nodeName = string.Empty;
|
|
switch (fileName)
|
|
{
|
|
case "input_0.pb":
|
|
nodeName = "unique_ids_raw_output___9:0";
|
|
break;
|
|
case "input_1.pb":
|
|
nodeName = "input_ids:0";
|
|
break;
|
|
case "input_2.pb":
|
|
nodeName = "input_mask:0";
|
|
break;
|
|
case "input_3.pb":
|
|
nodeName = "segment_ids:0";
|
|
break;
|
|
default:
|
|
throw new InvalidDataException($"Unhandled input file name: '{fileName}' for test_BERT_Squad");
|
|
}
|
|
return nodeName;
|
|
}
|
|
|
|
// The model actually has only 3 outputs, but the Zoo version has 4 files are supplied.
|
|
// The numbering of the output files does not match the order of outputs
|
|
// listed in the metadata.
|
|
|
|
// Previously, it worked because our CI test data version has matching
|
|
// tensor names that we could match to metadata after we load the tensor.
|
|
// But now, we need to know ahead of time what Onnx type we load, and thus match
|
|
// metadata with the test data file before loading. Protobuf can happily load whatever
|
|
// and give you garbage.
|
|
|
|
// Order in the metadata: unstack:1, unstack:0, unique_ids:0
|
|
// The files are in reverse order
|
|
private string MatchBertSquadOutputs(string fileName)
|
|
{
|
|
string nodeName = string.Empty;
|
|
switch (fileName)
|
|
{
|
|
case "output_0.pb": // Int64
|
|
nodeName = "unique_ids:0";
|
|
break;
|
|
case "output_1.pb":
|
|
nodeName = "unstack:0";
|
|
break;
|
|
case "output_2.pb":
|
|
nodeName = "unstack:1";
|
|
break;
|
|
default:
|
|
throw new InvalidDataException($"Unhandled output file name: '{fileName}' for test_BERT_Squad");
|
|
}
|
|
return nodeName;
|
|
}
|
|
|
|
[Theory(DisplayName = "TestPreTrainedModels")]
|
|
[MemberData(nameof(GetModelsForTest))]
|
|
[MemberData(nameof(GetSkippedModelForTest), Skip = "Skipped due to Error, please fix the error and enable the test")]
|
|
private void TestPreTrainedModels(string opsetDir, string modelName)
|
|
{
|
|
var opsetDirInfo = new DirectoryInfo(opsetDir);
|
|
var opset = opsetDirInfo.Name;
|
|
string onnxModelFileName = null;
|
|
|
|
var modelDir = new DirectoryInfo(Path.Combine(opsetDir, 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;
|
|
var outMeta = session.OutputMetadata;
|
|
string testDataDirNamePattern = "test_data*";
|
|
if (opset == "opset9" && modelName == "LSTM_Seq_lens_unpacked")
|
|
{
|
|
testDataDirNamePattern = "seq_lens*"; // discrepancy in data directory
|
|
}
|
|
foreach (var testDataDir in modelDir.EnumerateDirectories(testDataDirNamePattern))
|
|
{
|
|
var inputContainer = new List<NamedOnnxValue>(inMeta.Count);
|
|
var outputContainer = new List<NamedOnnxValue>(outMeta.Count);
|
|
foreach (var f in testDataDir.EnumerateFiles("input_*.pb"))
|
|
{
|
|
if (modelName == "keras_prelu_ImageNet_small" && opset == "opset9")
|
|
{
|
|
// The model has 1 input, match all file names (they are different in each data set)
|
|
// to the same input
|
|
var nodeName = "p_re_lu_3_input";
|
|
var nodeMeta = inMeta[nodeName];
|
|
inputContainer.Add(TestDataLoader.LoadOnnxValueFromFilePb(f.FullName, nodeName, nodeMeta));
|
|
}
|
|
else if (modelName == "test_BERT_Squad" && opset == "opset8")
|
|
{
|
|
string nodeName = MatchBertSquadInputs(f.Name);
|
|
var nodeMeta = inMeta[nodeName];
|
|
inputContainer.Add(TestDataLoader.LoadOnnxValueFromFilePb(f.FullName, nodeName, nodeMeta));
|
|
}
|
|
else
|
|
{
|
|
var nodeName = MatchInputOutputWithFile(f.Name, session, true, out NodeMetadata nodeMeta);
|
|
inputContainer.Add(TestDataLoader.LoadOnnxValueFromFilePb(f.FullName, nodeName, nodeMeta));
|
|
}
|
|
}
|
|
foreach (var f in testDataDir.EnumerateFiles("output_*.pb"))
|
|
{
|
|
if (modelName == "keras_prelu_ImageNet_small" && opset == "opset9")
|
|
{
|
|
// The model has 1 output, match all file names (they are different in each data set)
|
|
// to the same output
|
|
var nodeName = "p_re_lu_3/add:0";
|
|
var nodeMeta = outMeta[nodeName];
|
|
outputContainer.Add(TestDataLoader.LoadOnnxValueFromFilePb(f.FullName, nodeName, nodeMeta));
|
|
}
|
|
else if (modelName == "test_BERT_Squad" && opset == "opset8")
|
|
{
|
|
string nodeName = MatchBertSquadOutputs(f.Name);
|
|
var nodeMeta = outMeta[nodeName];
|
|
outputContainer.Add(TestDataLoader.LoadOnnxValueFromFilePb(f.FullName, nodeName, nodeMeta));
|
|
}
|
|
else
|
|
{
|
|
// Otherwise, just match trailing filename number to the input name -> metadata
|
|
var nodeName = MatchInputOutputWithFile(f.Name, session, false, out NodeMetadata nodeMeta);
|
|
outputContainer.Add(TestDataLoader.LoadOnnxValueFromFilePb(f.FullName, nodeName, nodeMeta));
|
|
}
|
|
}
|
|
|
|
using (var resultCollection = session.Run(inputContainer))
|
|
{
|
|
foreach (var result in resultCollection)
|
|
{
|
|
Assert.True(session.OutputMetadata.ContainsKey(result.Name));
|
|
NamedOnnxValue outputValue = null;
|
|
foreach (var o in outputContainer)
|
|
{
|
|
if (o.Name == result.Name)
|
|
{
|
|
outputValue = o;
|
|
break;
|
|
}
|
|
}
|
|
|
|
Assert.NotNull(outputValue);
|
|
|
|
var outputMeta = session.OutputMetadata[result.Name];
|
|
if (outputMeta.OnnxValueType == OnnxValueType.ONNX_TYPE_OPTIONAL)
|
|
{
|
|
outputMeta = outputMeta.AsOptionalMetadata().ElementMeta;
|
|
}
|
|
|
|
Assert.Equal(outputValue.ValueType, outputMeta.OnnxValueType);
|
|
|
|
switch (outputValue.ValueType)
|
|
{
|
|
case OnnxValueType.ONNX_TYPE_TENSOR: // Only Dense tensors now
|
|
{
|
|
VerifyTensorResults(outputMeta.ElementDataType, result, outputValue);
|
|
}
|
|
break;
|
|
case OnnxValueType.ONNX_TYPE_SEQUENCE:
|
|
{
|
|
VerifySequenceResults(result, outputValue, outputMeta);
|
|
}
|
|
break;
|
|
default:
|
|
Assert.True(false, $"TestPreTrainedModels cannot handle Onnxtype: {outputValue.ValueType}");
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
catch (Exception ex)
|
|
{
|
|
var msg = $"Opset {opset}, Model {modelName}: ModelFile = {onnxModelFileName} error = {ex.Message}";
|
|
if (ex.Message.Contains("ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions"))
|
|
{
|
|
// If the exception is thrown because the opset version of the test model is
|
|
// not supported by ONNXRuntime yet, then ignore the test and proceed.
|
|
// ORT allows commits from ONNX master and in such cases we do come across new opsets which are
|
|
// not supported in ORT yet. In order to force these tests to run set env var ALLOW_RELEASED_ONNX_OPSET_ONLY=0
|
|
output.WriteLine("Skipping the model test as the latest ONNX opset is not supported yet. Error Message: " + msg);
|
|
}
|
|
else
|
|
{
|
|
throw new Exception(msg + "\n" + ex.StackTrace);
|
|
}
|
|
}
|
|
}
|
|
|
|
private void VerifySequenceResults(NamedOnnxValue result, NamedOnnxValue expectedValue, NodeMetadata metaData)
|
|
{
|
|
var meta = metaData.AsSequenceMetadata();
|
|
var resultSequence = result.AsEnumerable<NamedOnnxValue>();
|
|
var expectedSequence = expectedValue.AsEnumerable<NamedOnnxValue>();
|
|
Assert.Equal(resultSequence.Count(), expectedSequence.Count());
|
|
|
|
foreach (var (resultItem, expectedItem) in resultSequence.Zip(expectedSequence, (r, e) => (r, e)))
|
|
{
|
|
Assert.Equal(resultItem.ValueType, expectedItem.ValueType);
|
|
Assert.Equal(resultItem.ValueType, meta.ElementMeta.OnnxValueType);
|
|
switch (resultItem.ValueType)
|
|
{
|
|
case OnnxValueType.ONNX_TYPE_TENSOR:
|
|
VerifyTensorResults(meta.ElementMeta.ElementDataType, resultItem, expectedItem);
|
|
break;
|
|
case OnnxValueType.ONNX_TYPE_SEQUENCE:
|
|
{
|
|
VerifySequenceResults(resultItem, expectedItem, meta.ElementMeta);
|
|
}
|
|
break;
|
|
default:
|
|
Assert.True(false, "VerifySequenceResults cannot handle Onnxtype: " + resultItem.ValueType.ToString());
|
|
break;
|
|
}
|
|
Assert.Equal(resultItem.AsTensor<float>(), expectedItem.AsTensor<float>(), new FloatComparer());
|
|
}
|
|
}
|
|
|
|
private void VerifyTensorResults(TensorElementType elementType, NamedOnnxValue result, NamedOnnxValue outputValue)
|
|
{
|
|
switch (elementType)
|
|
{
|
|
case TensorElementType.Float:
|
|
Assert.Equal(result.AsTensor<float>(), outputValue.AsTensor<float>(), new FloatComparer());
|
|
break;
|
|
case TensorElementType.Double:
|
|
Assert.Equal(result.AsTensor<double>(), outputValue.AsTensor<double>(), new DoubleComparer());
|
|
break;
|
|
case TensorElementType.Int32:
|
|
Assert.Equal(result.AsTensor<int>(), outputValue.AsTensor<int>(), new ExactComparer<int>());
|
|
break;
|
|
case TensorElementType.UInt32:
|
|
Assert.Equal(result.AsTensor<uint>(), outputValue.AsTensor<uint>(), new ExactComparer<uint>());
|
|
break;
|
|
case TensorElementType.Int16:
|
|
Assert.Equal(result.AsTensor<short>(), outputValue.AsTensor<short>(), new ExactComparer<short>());
|
|
break;
|
|
case TensorElementType.UInt16:
|
|
Assert.Equal(result.AsTensor<ushort>(), outputValue.AsTensor<ushort>(), new ExactComparer<ushort>());
|
|
break;
|
|
case TensorElementType.Int64:
|
|
Assert.Equal(result.AsTensor<long>(), outputValue.AsTensor<long>(), new ExactComparer<long>());
|
|
break;
|
|
case TensorElementType.UInt64:
|
|
Assert.Equal(result.AsTensor<ulong>(), outputValue.AsTensor<ulong>(), new ExactComparer<ulong>());
|
|
break;
|
|
case TensorElementType.UInt8:
|
|
Assert.Equal(result.AsTensor<byte>(), outputValue.AsTensor<byte>(), new ExactComparer<byte>());
|
|
break;
|
|
case TensorElementType.Int8:
|
|
Assert.Equal(result.AsTensor<sbyte>(), outputValue.AsTensor<sbyte>(), new ExactComparer<sbyte>());
|
|
break;
|
|
case TensorElementType.Bool:
|
|
Assert.Equal(result.AsTensor<bool>(), outputValue.AsTensor<bool>(), new ExactComparer<bool>());
|
|
break;
|
|
case TensorElementType.Float16:
|
|
Assert.Equal(result.AsTensor<Float16>(), outputValue.AsTensor<Float16>(), new Float16Comparer { tolerance = 2 });
|
|
break;
|
|
case TensorElementType.BFloat16:
|
|
Assert.Equal(result.AsTensor<BFloat16>(), outputValue.AsTensor<BFloat16>(), new BFloat16Comparer { tolerance = 2 });
|
|
break;
|
|
case TensorElementType.String:
|
|
Assert.Equal(result.AsTensor<string>(), outputValue.AsTensor<string>(), new ExactComparer<string>());
|
|
break;
|
|
default:
|
|
Assert.True(false, "TestPreTrainedModels does not yet support output of type: " + elementType.ToString());
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Hint: .NET Core 3.1 has a 'NativeLibrary' class that can be used to free the library handle
|
|
private void UnloadLibrary(IntPtr libraryHandle)
|
|
{
|
|
if (libraryHandle != IntPtr.Zero)
|
|
{
|
|
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
|
|
{
|
|
if (!FreeLibrary(libraryHandle))
|
|
{
|
|
throw new Exception("Could not unload the provided shared library using its handle");
|
|
}
|
|
}
|
|
|
|
else
|
|
{
|
|
// TODO: Deal with non-Windows platforms for the .NET Core use-case
|
|
}
|
|
}
|
|
}
|
|
|
|
private string GetCustomOpLibFullPath()
|
|
{
|
|
string libName = "custom_op_library.dll";
|
|
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.");
|
|
|
|
return libFullPath;
|
|
}
|
|
|
|
private void ValidateModelWithCustomOps(SessionOptions options)
|
|
{
|
|
string modelPath = "custom_op_test.onnx";
|
|
|
|
using (var session = new InferenceSession(modelPath, options))
|
|
{
|
|
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));
|
|
}
|
|
}
|
|
}
|
|
|
|
[SkipNonPackageTests(DisplayName = "TestRegisterCustomOpLibrary")]
|
|
private void TestRegisterCustomOpLibrary()
|
|
{
|
|
using (var option = new SessionOptions())
|
|
{
|
|
string libFullPath = GetCustomOpLibFullPath();
|
|
|
|
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);
|
|
}
|
|
|
|
var ortEnvInstance = OrtEnv.Instance();
|
|
string[] providers = ortEnvInstance.GetAvailableProviders();
|
|
if (Array.Exists(providers, provider => provider == "CUDAExecutionProvider"))
|
|
{
|
|
option.AppendExecutionProvider_CUDA(0);
|
|
}
|
|
|
|
ValidateModelWithCustomOps(option);
|
|
}
|
|
}
|
|
|
|
[SkipNonPackageTests(DisplayName = "TestRegisterCustomOpLibraryV2")]
|
|
private void TestRegisterCustomOpLibraryV2()
|
|
{
|
|
using (var option = new SessionOptions())
|
|
{
|
|
string libFullPath = GetCustomOpLibFullPath();
|
|
|
|
var ortEnvInstance = OrtEnv.Instance();
|
|
string[] providers = ortEnvInstance.GetAvailableProviders();
|
|
if (Array.Exists(providers, provider => provider == "CUDAExecutionProvider"))
|
|
{
|
|
option.AppendExecutionProvider_CUDA(0);
|
|
}
|
|
|
|
IntPtr libraryHandle = IntPtr.Zero;
|
|
try
|
|
{
|
|
option.RegisterCustomOpLibraryV2(libFullPath, out libraryHandle);
|
|
}
|
|
catch (Exception ex)
|
|
{
|
|
var msg = $"Failed to load custom op library {libFullPath}, error = {ex.Message}";
|
|
throw new Exception(msg + "\n" + ex.StackTrace);
|
|
}
|
|
|
|
ValidateModelWithCustomOps(option);
|
|
|
|
// Safe to unload the custom op shared library now
|
|
UnloadLibrary(libraryHandle);
|
|
}
|
|
}
|
|
|
|
// test registration by the alternative function name in the custom ops library.
|
|
// We use DllImport and Marshal.Prelink to make sure the library is loaded and
|
|
// the symbol is available.
|
|
internal static class CustomOpLibrary
|
|
{
|
|
internal const string ExtensionsDllName = "custom_op_library";
|
|
|
|
[DllImport(ExtensionsDllName, CharSet = CharSet.Ansi,
|
|
CallingConvention = CallingConvention.Winapi)]
|
|
public static extern IntPtr /* OrtStatus* */ RegisterCustomOpsAltName(
|
|
IntPtr /* OrtSessionOptions* */ sessionOptions,
|
|
ref OrtApiBase /* OrtApiBase* */ ortApiBase);
|
|
}
|
|
|
|
[SkipNonPackageTests(DisplayName = "TestRegisterCustomOpsWithFunction")]
|
|
private void TestRegisterCustomOpsWithFunction()
|
|
{
|
|
// TODO(scmckay): Validate on Linus and OS X. Marshal.Prelink seems to work differently on at least Linux.
|
|
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
|
|
{
|
|
using (var option = new SessionOptions())
|
|
{
|
|
try
|
|
{
|
|
Marshal.Prelink(typeof(CustomOpLibrary).GetMethod("RegisterCustomOpsAltName"));
|
|
option.RegisterCustomOpsUsingFunction("RegisterCustomOpsAltName");
|
|
}
|
|
catch (Exception ex)
|
|
{
|
|
var msg = $"Failed to load custom op library, error = {ex.Message}";
|
|
throw new Exception(msg + "\n" + ex.StackTrace);
|
|
}
|
|
|
|
var ortEnvInstance = OrtEnv.Instance();
|
|
string[] providers = ortEnvInstance.GetAvailableProviders();
|
|
if (Array.Exists(providers, provider => provider == "CUDAExecutionProvider"))
|
|
{
|
|
option.AppendExecutionProvider_CUDA(0);
|
|
}
|
|
|
|
ValidateModelWithCustomOps(option);
|
|
}
|
|
}
|
|
}
|
|
|
|
[Fact(DisplayName = "TestModelSerialization")]
|
|
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.
|
|
using (SessionOptions options = new SessionOptions())
|
|
{
|
|
options.OptimizedModelFilePath = modelOutputPath;
|
|
options.GraphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_BASIC;
|
|
using (var session = new InferenceSession(modelPath, options))
|
|
{
|
|
Assert.NotNull(session);
|
|
Assert.True(File.Exists(modelOutputPath));
|
|
}
|
|
}
|
|
}
|
|
|
|
// TestGpu() will test the CUDA EP on CUDA enabled builds and
|
|
// the DML EP on DML enabled builds
|
|
[GpuFact(DisplayName = "TestGpu")]
|
|
private void TestGpu()
|
|
{
|
|
var tuple = OpenSessionSqueezeNet(0); // run on deviceID 0
|
|
float[] expectedOutput = TestDataLoader.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());
|
|
}
|
|
}
|
|
|
|
[DllImport("kernel32", SetLastError = true)]
|
|
static extern IntPtr LoadLibrary(string lpFileName);
|
|
|
|
[DllImport("kernel32", CharSet = CharSet.Ansi)]
|
|
static extern UIntPtr GetProcAddress(IntPtr hModule, string procName);
|
|
|
|
[DllImport("kernel32.dll", CharSet = CharSet.Ansi)]
|
|
private static extern bool FreeLibrary(IntPtr hModule);
|
|
|
|
[Fact(DisplayName = "VerifyNativeMethodsExist")]
|
|
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_ROCM
|
|
,"OrtSessionOptionsAppendExecutionProvider_ROCM"
|
|
#endif
|
|
#if USE_DML
|
|
,"OrtSessionOptionsAppendExecutionProvider_DML"
|
|
#endif
|
|
#if USE_OPENVINO
|
|
,"OrtSessionOptionsAppendExecutionProvider_OpenVINO"
|
|
#endif
|
|
#if USE_TENSORRT
|
|
,"OrtSessionOptionsAppendExecutionProvider_Tensorrt"
|
|
#endif
|
|
#if USE_MIGRAPHX
|
|
,"OrtSessionOptionsAppendExecutionProvider_MIGraphX"
|
|
#endif
|
|
#if USE_NNAPI
|
|
,"OrtSessionOptionsAppendExecutionProvider_Nnapi"
|
|
#endif
|
|
};
|
|
IntPtr libraryHandle = IntPtr.Zero;
|
|
try
|
|
{
|
|
libraryHandle = LoadLibrary(module);
|
|
foreach (var ep in entryPointNames)
|
|
{
|
|
var x = GetProcAddress(libraryHandle, ep);
|
|
Assert.False(x == UIntPtr.Zero, $"Entrypoint {ep} not found in module {module}");
|
|
}
|
|
}
|
|
|
|
finally
|
|
{
|
|
UnloadLibrary(libraryHandle);
|
|
}
|
|
}
|
|
|
|
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;
|
|
}
|
|
}
|
|
}
|