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Added unit tests for diff input model types #82

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Added unit tests for diff input model types #82

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204b756
Added unit tests for diff input model types
jignparm Dec 4, 2018
0be6899
reverted yunsong model test
jignparm Dec 4, 2018
54b9978
minor fix
jignparm Dec 4, 2018
daeb53b
Removed yunsong model
jignparm Dec 4, 2018
d9a2ef9
Merge branch 'master' into jignparm/csharp_test_valid_input_model_types
jignparm Dec 4, 2018
b4e4ab3
Merge branch 'master' into jignparm/csharp_test_valid_input_model_types
jignparm Dec 5, 2018
ee8e9a3
Merge branch 'master' into jignparm/csharp_test_valid_input_model_types
jignparm Dec 5, 2018
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208 changes: 201 additions & 7 deletions csharp/test/Microsoft.ML.OnnxRuntime.Tests/InferenceTest.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -71,9 +71,9 @@ private void CanRunInferenceOnAModel()
var results = session.Run(container); // results is an IReadOnlyList<NamedOnnxValue> container

Assert.Equal(1, results.Count);

float[] expectedOutput = LoadTensorFromFile(@"bench.expected_out");
float errorMargin = 1e-6F;
float errorMargin = 1e-6F;
// validate the results
foreach (var r in results)
{
Expand All @@ -82,7 +82,7 @@ private void CanRunInferenceOnAModel()
var resultTensor = r.AsTensor<float>();
int[] expectedDimensions = { 1, 1000, 1, 1 }; // hardcoded for now for the test data
Assert.Equal(expectedDimensions.Length, resultTensor.Rank);

var resultDimensions = resultTensor.Dimensions;
for (int i = 0; i < expectedDimensions.Length; i++)
{
Expand Down Expand Up @@ -113,8 +113,8 @@ private void ThrowWrongInputName()
var container = new List<NamedOnnxValue>();
container.Add(NamedOnnxValue.CreateFromTensor<float>("wrong_name", tensor));
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
Assert.Equal("[ErrorCode:InvalidArgument] Missing required inputs: data_0", ex.Message);
session.Dispose();
Assert.Equal("[ErrorCode:InvalidArgument] Invalid Feed Input Names: wrong_name Valid input names are: data_0 ", ex.Message);
session.Dispose();
}

[Fact]
Expand Down Expand Up @@ -179,10 +179,205 @@ private void ThrowExtraInputs()
container.Add(nov1);
container.Add(nov2);
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
Assert.StartsWith("[ErrorCode:InvalidArgument] Invalid Feed Input Names: extra. Valid input names are: ", ex.Message);
Assert.Equal("[ErrorCode:InvalidArgument] The number of feeds is not same as the number of the model input, expect 1 got 2", ex.Message);
session.Dispose();
}

[Fact]
private void TestModelInputFloat()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_FLOAT.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var nov = NamedOnnxValue.CreateFromTensor("input",
new DenseTensor<float>(new float[] {1.0f, 2.0f, -3.0f, float.MinValue, float.MaxValue },
new int[] { 1, 5 }) );
container.Add(nov);
var res = session.Run(container);
}

[Fact(Skip = "Boolean tensor not supported yet")]
private void TestModelInputBOOL()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_BOOL.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<bool>(new bool[] { true, false, true, false, true }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<bool>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
[Fact]

private void TestModelInputINT32()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_INT32.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<int>(new int[] { 1, -2, -3, int.MinValue, int.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<int>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact]
private void TestModelInputDOUBLE()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_DOUBLE.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<double>(new double[] { 1.0, 2.0, -3.0, 5, 5},new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
//new DenseTensor<double>(new double[] { 1.0, 2.0, -3.0, double.MinValue, double.MaxValue},
//new int[] { 1, 5 }));
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<double>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact(Skip = "String tensor not supported yet")]
private void TestModelInputSTRING()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_STRING.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<string>(new string[] { "a", "c", "d", "z", "f" }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<string>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact(Skip = "Int8 not supported yet")]
private void TestModelInputINT8()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_INT8.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<sbyte>(new sbyte[] { 1, 2, -3, sbyte.MinValue, sbyte.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<sbyte>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact]
private void TestModelInputUINT8()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_UINT8.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<byte>(new byte[] { 1, 2, 3, byte.MinValue, byte.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<byte>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact]
private void TestModelInputUINT16()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_UINT16.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<UInt16>(new UInt16[] { 1, 2, 3, UInt16.MinValue, UInt16.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<UInt16>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact]
private void TestModelInputINT16()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_INT16.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<Int16>(new Int16[] { 1, 2, 3, Int16.MinValue, Int16.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<Int16>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact]
private void TestModelInputINT64()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_INT64.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<Int64>(new Int64[] { 1, 2, -3, Int64.MinValue, Int64.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<Int64>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact]
private void TestModelInputUINT32()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_UINT32.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<UInt32>(new UInt32[] { 1, 2, 3, UInt32.MinValue, UInt32.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<UInt32>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
[Fact]
private void TestModelInputUINT64()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_UINT64.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<UInt64>(new UInt64[] { 1, 2, 3, UInt64.MinValue, UInt64.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<UInt64>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact(Skip = "Boolean FLOAT16 not available in C#")]
private void TestModelInputFLOAT16()
{
// model takes 1x5 input of fixed type, echoes back
string modelPath = Directory.GetCurrentDirectory() + @"\test_types_FLOAT16.onnx";
var session = new InferenceSession(modelPath);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<float>(new float[] { 1.0f, 2.0f, -3.0f, float.MinValue, float.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res = session.Run(container);
var tensorOut = res.First().AsTensor<float>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}

[Fact]
private void Yunsong()
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should we remove this now?

{
Expand Down Expand Up @@ -404,7 +599,6 @@ private void Yunsong()
}



static float[] LoadTensorFromFile(string filename)
{
var tensorData = new List<float>();
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