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InferenceTest.netcore.cs
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using Microsoft.ML.OnnxRuntime.Tensors;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
using System.Text.RegularExpressions;
using Xunit;
namespace Microsoft.ML.OnnxRuntime.Tests
{
/// <summary>
/// This is compensate for the absence of string.Contains() in .NET Standard 2.0
/// Contains(String, StringComparison)
/// </summary>
public static class StringExtensions
{
public static bool Contains(this String str, String substring,
StringComparison comp)
{
if (substring == null)
throw new ArgumentNullException("substring",
"substring cannot be null.");
else if (!Enum.IsDefined(typeof(StringComparison), comp))
throw new ArgumentException("comp is not a member of StringComparison",
"comp");
return str.IndexOf(substring, comp) >= 0;
}
}
public partial class InferenceTest
{
private const string module = "onnxruntime.dll";
private const string propertiesFile = "Properties.txt";
[Fact(DisplayName = "CanCreateAndDisposeSessionWithModelPath")]
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 nodeMeta
Assert.True(session.InputMetadata.ContainsKey("data_0")); // input nodeMeta 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 nodeMeta
Assert.True(session.OutputMetadata.ContainsKey("softmaxout_1")); // output nodeMeta 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]);
}
}
}
#if USE_CUDA
[Fact(DisplayName = "TestCUDAProviderOptions")]
private void TestCUDAProviderOptions()
{
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
string defaultDeviceId = "0";
string deviceIdFromEnv = System.Environment.GetEnvironmentVariable("OnnxruntimeTestGpuDeviceId");
if (!string.IsNullOrEmpty(deviceIdFromEnv) && int.TryParse(deviceIdFromEnv, out int deviceId) && deviceId >= 0)
{
defaultDeviceId = deviceIdFromEnv;
output.WriteLine($"Parsed ID: {deviceIdFromEnv}");
}
using (var cleanUp = new DisposableListTest<IDisposable>())
{
var cudaProviderOptions = new OrtCUDAProviderOptions();
cleanUp.Add(cudaProviderOptions);
var providerOptionsDict = new Dictionary<string, string>();
providerOptionsDict["device_id"] = defaultDeviceId;
// 256MB
providerOptionsDict["gpu_mem_limit"] = "268435456";
providerOptionsDict["arena_extend_strategy"] = "kSameAsRequested";
providerOptionsDict["cudnn_conv_algo_search"] = "DEFAULT";
providerOptionsDict["do_copy_in_default_stream"] = "1";
providerOptionsDict["cudnn_conv_use_max_workspace"] = "1";
providerOptionsDict["cudnn_conv1d_pad_to_nc1d"] = "1";
cudaProviderOptions.UpdateOptions(providerOptionsDict);
var resultProviderOptionsDict = new Dictionary<string, string>();
ProviderOptionsValueHelper.StringToDict(cudaProviderOptions.GetOptions(), resultProviderOptionsDict);
// test provider options configuration
string value;
value = resultProviderOptionsDict["device_id"];
Assert.Equal("0", value);
value = resultProviderOptionsDict["gpu_mem_limit"];
Assert.Equal("268435456", value);
value = resultProviderOptionsDict["arena_extend_strategy"];
Assert.Equal("kSameAsRequested", value);
value = resultProviderOptionsDict["cudnn_conv_algo_search"];
Assert.Equal("DEFAULT", value);
value = resultProviderOptionsDict["do_copy_in_default_stream"];
Assert.Equal("1", value);
value = resultProviderOptionsDict["cudnn_conv_use_max_workspace"];
Assert.Equal("1", value);
value = resultProviderOptionsDict["cudnn_conv1d_pad_to_nc1d"];
Assert.Equal("1", value);
// test correctness of provider options
SessionOptions options = SessionOptions.MakeSessionOptionWithCudaProvider(cudaProviderOptions);
cleanUp.Add(options);
var session = new InferenceSession(modelPath, options);
cleanUp.Add(session);
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
float[] inputData = TestDataLoader.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));
}
session.Run(container);
}
}
#endif
#if USE_TENSORRT
[Fact(DisplayName = "CanRunInferenceOnAModelWithTensorRT")]
private void CanRunInferenceOnAModelWithTensorRT()
{
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
int deviceId = 0;
string deviceIdStr = System.Environment.GetEnvironmentVariable("ONNXRUNTIME_TEST_GPU_DEVICE_ID");
if (!string.IsNullOrEmpty(deviceIdStr) && int.TryParse(deviceIdStr, out int parsedValue) && parsedValue >= 0)
{
deviceId = parsedValue;
output.WriteLine($"Parsed ID: {parsedValue}");
}
using (var cleanUp = new DisposableListTest<IDisposable>())
{
SessionOptions options = SessionOptions.MakeSessionOptionWithTensorrtProvider(deviceId);
cleanUp.Add(options);
var session = new InferenceSession(modelPath, options);
cleanUp.Add(session);
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
float[] inputData = TestDataLoader.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));
}
using (var results = session.Run(container))
{
ValidateRunResults(results);
}
}
}
[Fact(DisplayName = "TestTensorRTProviderOptions")]
private void TestTensorRTProviderOptions()
{
string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
string calTablePath = "squeezenet_calibration.flatbuffers";
string enginePath = "./";
string engineDecrptLibPath = "engine_decryp";
string defaultDeviceId = "0";
string deviceIdFromEnv = System.Environment.GetEnvironmentVariable("OnnxruntimeTestGpuDeviceId");
if (!string.IsNullOrEmpty(deviceIdFromEnv) && int.TryParse(deviceIdFromEnv, out int deviceId) && deviceId >= 0)
{
defaultDeviceId = deviceIdFromEnv;
output.WriteLine($"Parsed ID: {deviceIdFromEnv}");
}
using (var cleanUp = new DisposableListTest<IDisposable>())
{
var trtProviderOptions = new OrtTensorRTProviderOptions();
cleanUp.Add(trtProviderOptions);
var providerOptionsDict = new Dictionary<string, string>();
providerOptionsDict["device_id"] = defaultDeviceId;
providerOptionsDict["trt_fp16_enable"] = "1";
providerOptionsDict["trt_int8_enable"] = "1";
providerOptionsDict["trt_int8_calibration_table_name"] = calTablePath;
providerOptionsDict["trt_engine_cache_enable"] = "1";
providerOptionsDict["trt_engine_cache_path"] = enginePath;
providerOptionsDict["trt_engine_decryption_enable"] = "0";
providerOptionsDict["trt_engine_decryption_lib_path"] = engineDecrptLibPath;
trtProviderOptions.UpdateOptions(providerOptionsDict);
var resultProviderOptionsDict = new Dictionary<string, string>();
ProviderOptionsValueHelper.StringToDict(trtProviderOptions.GetOptions(), resultProviderOptionsDict);
// test provider options configuration
string value;
value = resultProviderOptionsDict["device_id"];
Assert.Equal(defaultDeviceId, value);
value = resultProviderOptionsDict["trt_fp16_enable"];
Assert.Equal("1", value);
value = resultProviderOptionsDict["trt_int8_enable"];
Assert.Equal("1", value);
value = resultProviderOptionsDict["trt_int8_calibration_table_name"];
Assert.Equal(calTablePath, value);
value = resultProviderOptionsDict["trt_engine_cache_enable"];
Assert.Equal("1", value);
value = resultProviderOptionsDict["trt_engine_cache_path"];
Assert.Equal(enginePath, value);
value = resultProviderOptionsDict["trt_engine_decryption_enable"];
Assert.Equal("0", value);
value = resultProviderOptionsDict["trt_engine_decryption_lib_path"];
Assert.Equal(engineDecrptLibPath, value);
// test correctness of provider options
SessionOptions options = SessionOptions.MakeSessionOptionWithTensorrtProvider(trtProviderOptions);
cleanUp.Add(options);
var session = new InferenceSession(modelPath, options);
cleanUp.Add(session);
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
float[] inputData = TestDataLoader.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));
}
session.Run(container);
}
}
#endif
private static Func<DirectoryInfo, IEnumerable<DirectoryInfo>> getOpsetDirectories = delegate (DirectoryInfo modelsDirInfo)
{
return modelsDirInfo.EnumerateDirectories("opset*", SearchOption.AllDirectories);
};
private static Dictionary<string, string> GetSkippedModels(DirectoryInfo modelsDirInfo)
{
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_tiny_yolov2", "Tolerance level for float16 is not known. We now support fp16." },
{ "fp16_test_tiny_yolov2", "ImageScaler is not a registered function/op"},
{ "fp16_coreml_FNS-Candy", "ImageScaler is not a registered function/op" },
{ "fp16_coreml_LinearRegression_NYCTaxi", "Error in Node:featureVectorizer : No Op registered for FeatureVectorizer with domain_version of 1"},
{ "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" },
{ "BERT_Squad", "Could not find an implementation for the nodeMeta 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" },
{ "cntk_simple_seg", "Bad onnx test output caused by wrong SAME_UPPER/SAME_LOWER for ConvTranspose" },
{ "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" },
{ "test_maxunpool_export_with_output_shape", "results mismatch"},
{ "test_min_int8", "Could not find an implementation for Min(13) node with name"},
{ "test_min_uint8", "Could not find an implementation for Min(13) node with name"},
{ "test_min_int16", "Could not find an implementation for Min(13) node with name"},
{ "test_min_uint16", "Could not find an implementation for Min(13) node with name"},
{ "test_max_int8", "Could not find an implementation for Max(13) node with name"},
{ "test_max_uint8", "Could not find an implementation for Max(13) node with name"},
{ "test_max_int16", "Could not find an implementation for Max(13) node with name"},
{ "test_max_uint16", "Could not find an implementation for Max(13) nodeMeta with name '"},
{ "test_mul_uint8", "Could not find an implementation for Mul(14) node with name" },
{ "test_bitshift_right_uint16", "Could not find an implementation for BitShift(11) nodeMeta with name ''"},
{ "test_bitshift_left_uint16", "Could not find an implementation for BitShift(11)"},
{ "test_pow_types_float32_uint64", "Could not find an implementation for Pow(15) node with name ''"},
{ "test_pow_types_float32_uint32", "Could not find an implementation for Pow(15) node with name ''"},
{ "test_resize_downsample_scales_cubic_align_corners", "Results mismatch"},
{ "test_resize_downsample_scales_linear_align_corners", "Results mismatch"},
{ "test_gru_batchwise", "batchwise operations not supported"},
{ "test_lstm_batchwise", "Batchwise recurrent operations(layout == 1) are not supported.If you need support create a github issue with justification."},
{ "test_simple_rnn_batchwise", "batchwise operations not supported"},
{ "test_batchnorm_example_training_mode", "opset14 version not implemented yet"},
{ "test_bernoulli", "random generator, results mismatch"},
{ "test_bernoulli_seed", "random generator, results mismatch"},
{ "test_bernoulli_double", "random generator, results mismatch"},
{ "test_bernoulli_expanded", "random generator, results mismatch"},
{ "test_bernoulli_seed_expanded", "random generator, results mismatch"},
{ "test_bernoulli_double_expanded", "random generator, results mismatch"},
// the expansion of Softplus uses Exp(1). ORT has a Softplus kernel, so testing the expansion is
// unnecessary and fails as ORT support for Exp started at opset 6 (as ORT didn't exist until opset 7).
{ "test_clip_default_int8_max_expanded", "Could not find an implementation for Less(13) nodeMeta with name ''" },
{ "test_softplus_expanded", "Could not find an implementation for Exp(1) node with name ''"},
{ "test_softplus_example_expanded", "Could not find an implementation for Exp(1) node with name ''"},
{ "test_div_uint8", "Could not find an implementation for Div(14) nodeMeta with name ''"},
{ "test_add_uint8", "Opset18 Could not find an implementation for Add(14) nodeMeta with name ''"},
{ "test_col2im_pads", "Results mismatch due to a typo in test data"},
{ "test_optional_has_element_empty_optional_input", "OptionalProto test metadata. Unable to load 'optional_input' optional element type of: Undefined type"},
{ "test_loop13_seq", "3rd input is an empty sequence. Ort API does not tolerate empty seq: Number of values should be at least 1" },
// Training tests
{ "BERT-Squad-int8", "training domain"},
{ "YOLOv3-12-int8", "training_domain"},
{ "test_training_dropout_default", "results mismatch"},
{ "test_training_dropout_default_mask", "Results mismatch"},
{ "test_training_dropout", "results mismatch"},
{ "test_training_dropout_mask", "results mismatch."},
{ "test_momentum", "ai.onnx.preview.training:Momentum(-1) is not a registered function/op"},
{ "test_momentum_multiple", "ai.onnx.preview.training:Momentum(-1) is not a registered function/op"},
{ "test_nesterov_momentum", "ai.onnx.preview.training:Momentum(-1) is not a registered function/op"},
{ "test_adam", "ai.onnx.preview.training:Adam(-1) is not a registered function/op"},
{ "test_adam_multiple", "ai.onnx.preview.training:Adam(-1) is not a registered function/op"},
{ "test_adagrad", "ai.onnx.preview.training:Adagrad(-1) is not a registered function/op"},
{ "test_adagrad_multiple", "ai.onnx.preview.training:Adagrad(-1) is not a registered function/op"},
{ "test_zfnet512", "skip it as ZFNET-512"},
};
// 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";
}
// Skip traditional ML models
var disableMlOpsEnvVar = Environment.GetEnvironmentVariable("DisableMlOps");
var isMlOpsDisabled = (disableMlOpsEnvVar != null) ? disableMlOpsEnvVar.Equals("ON") : false;
if (isMlOpsDisabled)
{
foreach (var opsetDir in getOpsetDirectories(modelsDirInfo))
{
foreach (var modelDir in opsetDir.EnumerateDirectories())
{
var modelDirName = modelDir.Name;
if (modelDirName.StartsWith("scikit_") ||
modelDirName.StartsWith("libsvm_") ||
modelDirName.StartsWith("coreml_") ||
modelDirName.StartsWith("keras2coreml_") ||
modelDirName.StartsWith("XGBoost_"))
{
skipModels[modelDirName] = "Fails when ML ops are disabled";
}
} //model
} //opset
}
// 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["GPT2_LM_HEAD"] = "System out of memory";
skipModels["GPT2"] = "System out of memory";
skipModels["test_GPT2"] = "System out of memory";
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["ZFNet-512"] = "System out of memory";
skipModels["test_bvlc_reference_caffenet"] = "System out of memory";
skipModels["coreml_VGG16_ImageNet"] = "System out of memory";
skipModels["test_ssd"] = "System out of memory";
skipModels["roberta_sequence_classification"] = "System out of memory";
// models from model zoo
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;
}
private const string keras_prelu_ImageNet_small_nodeName_Input = "p_re_lu_3_input";
private const string keras_prelu_ImageNet_small_nodeName_Output = "p_re_lu_3/add:0";
private void LoadInputData<T>(string opset, string modelName,
DirectoryInfo testDataDir,
InferenceSession session,
IList<T> inputContainer,
Func<string, string, NodeMetadata, T> loader)
{
var inMeta = session.InputMetadata;
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 = keras_prelu_ImageNet_small_nodeName_Input;
var nodeMeta = inMeta[nodeName];
inputContainer.Add(loader(f.FullName, nodeName, nodeMeta));
}
else if (modelName == "test_BERT_Squad" && opset == "opset8")
{
string nodeName = MatchBertSquadInputs(f.Name);
var nodeMeta = inMeta[nodeName];
inputContainer.Add(loader(f.FullName, nodeName, nodeMeta));
}
else
{
var nodeName = MatchInputOutputWithFile(f.Name, session, true, out NodeMetadata nodeMeta);
inputContainer.Add(loader(f.FullName, nodeName, nodeMeta));
}
}
}
private void LoadOutputData<T>(string opset, string modelName,
DirectoryInfo testDataDir,
InferenceSession session,
IList<T> outputContainer,
Func<string, string, NodeMetadata, T> loader)
{
var outMeta = session.OutputMetadata;
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 = keras_prelu_ImageNet_small_nodeName_Output;
var nodeMeta = outMeta[nodeName];
outputContainer.Add(loader(f.FullName, nodeName, nodeMeta));
}
else if (modelName == "test_BERT_Squad" && opset == "opset8")
{
string nodeName = MatchBertSquadOutputs(f.Name);
var nodeMeta = outMeta[nodeName];
outputContainer.Add(loader(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(loader(f.FullName, nodeName, nodeMeta));
}
}
}
private void RunPretrainedModel(InferenceSession session,
IReadOnlyList<NamedOnnxValue> inputContainer, IReadOnlyList<NamedOnnxValue> outputContainer)
{
var outMeta = session.OutputMetadata;
var orderedOutputNames = new List<string>(outputContainer.Count);
foreach (var output in outputContainer)
{
orderedOutputNames.Add(output.Name);
}
using (var resultCollection = session.Run(inputContainer, orderedOutputNames))
{
Assert.Equal(outputContainer.Count, resultCollection.Count);
for (int i = 0; i < resultCollection.Count; ++i)
{
var result = resultCollection[i];
var outputValue = outputContainer[i];
Assert.NotNull(outputValue);
Assert.Equal(result.Name, outputValue.Name);
var outputMeta = outMeta[outputValue.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;
}
}
}
}
private void RunPretrainedModel(InferenceSession session, RunOptions runOptions,
IReadOnlyList<DisposableTestPair<OrtValue>> inputContainer,
IReadOnlyList<DisposableTestPair<OrtValue>> outputContainer)
{
var outMeta = session.OutputMetadata;
var orderedInputNames = new List<string>(inputContainer.Count);
var orderdedInputs = new List<OrtValue>(inputContainer.Count);
foreach(var pair in inputContainer)
{
orderedInputNames.Add(pair.Key);
orderdedInputs.Add(pair.Value);
}
var orderedOutputNames = new List<string>(outputContainer.Count);
var orderedOutputs = new List<OrtValue>(outputContainer.Count);
foreach (var pair in outputContainer)
{
orderedOutputNames.Add(pair.Key);
orderedOutputs.Add(pair.Value);
}
using (var results = session.Run(runOptions, orderedInputNames, orderdedInputs, orderedOutputNames))
{
Assert.Equal(outMeta.Count, results.Count);
Assert.Equal(outputContainer.Count, results.Count);
for (int i = 0; i < outputContainer.Count; ++i)
{
var resultValue = results[i];
var expectedValue = outputContainer[i].Value;
var outputMeta = outMeta[orderedOutputNames[i]];
if (outputMeta.OnnxValueType == OnnxValueType.ONNX_TYPE_OPTIONAL)
{
outputMeta = outputMeta.AsOptionalMetadata().ElementMeta;
}
if (outputMeta.OnnxValueType == OnnxValueType.ONNX_TYPE_TENSOR)
{
VerifyTensorResults(outputMeta.ElementDataType, resultValue, expectedValue);
}
else if (outputMeta.OnnxValueType == OnnxValueType.ONNX_TYPE_SEQUENCE)
{
VerifySequenceResults(resultValue, expectedValue, outputMeta);
}
else
{
Assert.True(false, $"TestPreTrainedModels cannot handle Onnxtype: {outputMeta.OnnxValueType}");
}
}
}
}
[Theory(DisplayName = "TestPretrainedModelsWithOrtValue")]
[MemberData(nameof(GetModelsForTest))]
[MemberData(nameof(GetSkippedModelForTest), Skip = "Skipped due to Error, please fix the error and enable the test")]
public void TestPretrainedModelsWithOrtValue(string opsetDir, string modelName)
{
TestPreTrainedModels(opsetDir, modelName, true);
}
[Theory(DisplayName = "TestPreTrainedModels")]
[MemberData(nameof(GetModelsForTest))]
[MemberData(nameof(GetSkippedModelForTest), Skip = "Skipped due to Error, please fix the error and enable the test")]
private void TestPreTrainedModels(string opsetDir, string modelName, bool useOrtValueAPIs = false)
{
var opsetDirInfo = new DirectoryInfo(opsetDir);
var opset = opsetDirInfo.Name;
string onnxModelFileName = null;
var modelDir = new DirectoryInfo(Path.Combine(opsetDir, modelName));
try
{
var onnxModelNames = modelDir.GetFiles("*.onnx");
bool validModelFound = false;
if (onnxModelNames.Length > 0)
{
// TODO remove file "._resnet34v2.onnx" from test set
for (int i = 0; i < onnxModelNames.Length; i++)
{
if (onnxModelNames[i].Name != "._resnet34v2.onnx")
{
onnxModelNames[0] = onnxModelNames[i];
validModelFound = true;
}
}
}
if (validModelFound)
{
onnxModelFileName = Path.Combine(modelDir.FullName, onnxModelNames[0].Name);
}
else
{
var modelNamesList = string.Join(",", onnxModelNames.Select(x => x.ToString()));
throw new Exception($"Opset {opset} Model {modelName}. Can't determine model file name. Found these :{modelNamesList}");
}
using(var runOptions = new RunOptions())
using (var session = new InferenceSession(onnxModelFileName))
{
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))
{
if (useOrtValueAPIs)
{
using (var inputOrtValues = new DisposableListTest<DisposableTestPair<OrtValue>>(session.InputMetadata.Count))
using (var outputOrtValues = new DisposableListTest<DisposableTestPair<OrtValue>>(session.OutputMetadata.Count))
{
LoadInputData(opset, modelName, testDataDir, session, inputOrtValues, TestDataLoader.LoadOrtValueFromFilePb);
LoadOutputData(opset, modelName, testDataDir, session, outputOrtValues, TestDataLoader.LoadOrtValueFromFilePb);
RunPretrainedModel(session, runOptions, inputOrtValues, outputOrtValues);
}
}
else
{
var inputContainer = new List<NamedOnnxValue>(session.InputMetadata.Count);
LoadInputData(opset, modelName, testDataDir, session, inputContainer, TestDataLoader.LoadOnnxValueFromFilePb);
var outputContainer = new List<NamedOnnxValue>(session.OutputMetadata.Count);
LoadOutputData(opset, modelName, testDataDir, session, outputContainer, TestDataLoader.LoadOnnxValueFromFilePb);
RunPretrainedModel(session, inputContainer, outputContainer);
}
}
}
}
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 static 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 static void VerifyTensorResults(TensorElementType elementType, NamedOnnxValue result, NamedOnnxValue expectedValue)
{
switch (elementType)
{
case TensorElementType.Float:
Assert.Equal(expectedValue.AsTensor<float>(), result.AsTensor<float>(), new FloatComparer());
break;
case TensorElementType.Double:
Assert.Equal(expectedValue.AsTensor<double>(), result.AsTensor<double>(), new DoubleComparer());
break;
case TensorElementType.Int32:
Assert.Equal(expectedValue.AsTensor<int>(), result.AsTensor<int>(), new ExactComparer<int>());
break;
case TensorElementType.UInt32:
Assert.Equal(expectedValue.AsTensor<uint>(), result.AsTensor<uint>(), new ExactComparer<uint>());
break;
case TensorElementType.Int16:
Assert.Equal(expectedValue.AsTensor<short>(), result.AsTensor<short>(), new ExactComparer<short>());
break;
case TensorElementType.UInt16:
Assert.Equal(expectedValue.AsTensor<ushort>(), result.AsTensor<ushort>(), new ExactComparer<ushort>());
break;
case TensorElementType.Int64:
Assert.Equal(expectedValue.AsTensor<long>(), result.AsTensor<long>(), new ExactComparer<long>());
break;
case TensorElementType.UInt64:
Assert.Equal(expectedValue.AsTensor<ulong>(), result.AsTensor<ulong>(), new ExactComparer<ulong>());
break;
case TensorElementType.UInt8:
Assert.Equal(expectedValue.AsTensor<byte>(), result.AsTensor<byte>(), new ExactComparer<byte>());
break;
case TensorElementType.Int8:
Assert.Equal(result.AsTensor<sbyte>(), result.AsTensor<sbyte>(), new ExactComparer<sbyte>());
break;
case TensorElementType.Bool:
Assert.Equal(expectedValue.AsTensor<bool>(), result.AsTensor<bool>(), new ExactComparer<bool>());
break;
case TensorElementType.Float16:
Assert.Equal(expectedValue.AsTensor<Float16>(), result.AsTensor<Float16>(), new Float16Comparer { tolerance = 2 });
break;
case TensorElementType.BFloat16:
Assert.Equal(expectedValue.AsTensor<BFloat16>(), result.AsTensor<BFloat16>(), new BFloat16Comparer { tolerance = 2 });
break;
case TensorElementType.String:
Assert.Equal(expectedValue.AsTensor<string>(), result.AsTensor<string>(), new ExactComparer<string>());
break;
default:
Assert.True(false, "TestPreTrainedModels does not yet support output of type: " + elementType.ToString());
break;
}
}
private static void VerifySequenceResults(OrtValue resultSequence, OrtValue expectedSequence, NodeMetadata metaData)
{
var allocator = OrtAllocator.DefaultInstance;
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, resultSequence.OnnxType);
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, expectedSequence.OnnxType);
var elementMeta = metaData.AsSequenceMetadata().ElementMeta;
var resultCount = resultSequence.GetValueCount();
Assert.Equal(expectedSequence.GetValueCount(), resultCount);
using (var cleanUp = new DisposableListTest<IDisposable>())
{
for (int i = 0; i < resultCount; ++i)
{
var resultItem = resultSequence.GetValue(i, allocator);
cleanUp.Add(resultItem);
var expectedItem = expectedSequence.GetValue(i, allocator);
cleanUp.Add(expectedItem);
Assert.Equal(elementMeta.OnnxValueType, expectedItem.OnnxType);
Assert.Equal(elementMeta.OnnxValueType, resultItem.OnnxType);
switch (elementMeta.OnnxValueType)
{
case OnnxValueType.ONNX_TYPE_TENSOR:
VerifyTensorResults(elementMeta.ElementDataType, resultItem, expectedItem);
break;
case OnnxValueType.ONNX_TYPE_SEQUENCE:
{
VerifySequenceResults(resultItem, expectedItem, elementMeta);
}
break;
default:
Assert.True(false, $"VerifySequenceResults cannot handle Onnxtype: {elementMeta.OnnxValueType}");
break;
}
}
}
}
private static void VerifyTensorResults(TensorElementType expectedElementType, OrtValue result, OrtValue expectedValue)
{
Assert.True(result.IsTensor);
Assert.True(expectedValue.IsTensor);
var resultTypeShape = result.GetTensorTypeAndShape();
var expectedTypeShape = expectedValue.GetTensorTypeAndShape();
Assert.Equal(expectedElementType, resultTypeShape.ElementDataType);
Assert.Equal(expectedElementType, expectedTypeShape.ElementDataType);
Assert.Equal(expectedTypeShape.Shape, resultTypeShape.Shape);
if (expectedElementType == TensorElementType.String)
{
var resStrings = result.GetStringTensorAsArray();
var expStrings = expectedValue.GetStringTensorAsArray();
Assert.Equal(expStrings, resStrings);
return;
}
switch (expectedElementType)
{
case TensorElementType.Float:
Assert.Equal(expectedValue.GetTensorDataAsSpan<float>().ToArray(), result.GetTensorDataAsSpan<float>().ToArray(),
new FloatComparer());
break;
case TensorElementType.Double:
Assert.Equal(expectedValue.GetTensorDataAsSpan<double>().ToArray(), result.GetTensorDataAsSpan<double>().ToArray(),
new DoubleComparer());
break;
case TensorElementType.Int32:
Assert.Equal(expectedValue.GetTensorDataAsSpan<int>().ToArray(), result.GetTensorDataAsSpan<int>().ToArray(), new ExactComparer<int>());
break;
case TensorElementType.UInt32:
Assert.Equal(expectedValue.GetTensorDataAsSpan<uint>().ToArray(), result.GetTensorDataAsSpan<uint>().ToArray(), new ExactComparer<uint>());
break;
case TensorElementType.Int16:
Assert.Equal(expectedValue.GetTensorDataAsSpan<short>().ToArray(), result.GetTensorDataAsSpan<short>().ToArray(), new ExactComparer<short>());
break;
case TensorElementType.UInt16:
Assert.Equal(expectedValue.GetTensorDataAsSpan<ushort>().ToArray(), result.GetTensorDataAsSpan<ushort>().ToArray(), new ExactComparer<ushort>());
break;
case TensorElementType.Int64:
Assert.Equal(expectedValue.GetTensorDataAsSpan<long>().ToArray(), result.GetTensorDataAsSpan<long>().ToArray(), new ExactComparer<long>());
break;
case TensorElementType.UInt64:
Assert.Equal(expectedValue.GetTensorDataAsSpan<ulong>().ToArray(), result.GetTensorDataAsSpan<ulong>().ToArray(), new ExactComparer<ulong>());
break;
case TensorElementType.UInt8:
Assert.Equal(expectedValue.GetTensorDataAsSpan<byte>().ToArray(), result.GetTensorDataAsSpan<byte>().ToArray(), new ExactComparer<byte>());
break;
case TensorElementType.Int8:
Assert.Equal(expectedValue.GetTensorDataAsSpan<sbyte>().ToArray(), result.GetTensorDataAsSpan<sbyte>().ToArray(), new ExactComparer<sbyte>());
break;
case TensorElementType.Bool: