[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // x86 fails with "An attempt was made to load a program with an incorrect format."
void TestSimpleCase()
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
var modelFile = "squeezenet/00000001/model.onnx";
var samplevector = GetSampleArrayData();
var dataView = ComponentCreation.CreateDataView(Env,
new TestData[] {
new TestData()
{
data_0 = samplevector
},
new TestData()
{
data_0 = samplevector
}
});
var xyData = new List <TestDataXY> {
new TestDataXY()
{
A = new float[inputSize]
}
};
var stringData = new List <TestDataDifferntType> {
new TestDataDifferntType()
{
data_0 = new string[inputSize]
}
};
var sizeData = new List <TestDataSize> {
new TestDataSize()
{
data_0 = new float[2]
}
};
var pipe = new OnnxScoringEstimator(Env, modelFile, new[] { "data_0" }, new[] { "softmaxout_1" });
var invalidDataWrongNames = ComponentCreation.CreateDataView(Env, xyData);
var invalidDataWrongTypes = ComponentCreation.CreateDataView(Env, stringData);
var invalidDataWrongVectorSize = ComponentCreation.CreateDataView(Env, sizeData);
TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
pipe.GetOutputSchema(SchemaShape.Create(invalidDataWrongVectorSize.Schema));
try
{
pipe.Fit(invalidDataWrongVectorSize);
Assert.False(true);
}
catch (ArgumentOutOfRangeException) { }
catch (InvalidOperationException) { }
}
void TestSimpleCase()
{
var modelFile = "squeezenet/00000001/model.onnx";
var samplevector = GetSampleArrayData();
var dataView = ML.Data.ReadFromEnumerable(
new TestData[] {
new TestData()
{
data_0 = samplevector
},
new TestData()
{
data_0 = samplevector
}
});
var xyData = new List <TestDataXY> {
new TestDataXY()
{
A = new float[inputSize]
}
};
var stringData = new List <TestDataDifferntType> {
new TestDataDifferntType()
{
data_0 = new string[inputSize]
}
};
var sizeData = new List <TestDataSize> {
new TestDataSize()
{
data_0 = new float[2]
}
};
var pipe = new OnnxScoringEstimator(Env, new[] { "softmaxout_1" }, new[] { "data_0" }, modelFile);
var invalidDataWrongNames = ML.Data.ReadFromEnumerable(xyData);
var invalidDataWrongTypes = ML.Data.ReadFromEnumerable(stringData);
var invalidDataWrongVectorSize = ML.Data.ReadFromEnumerable(sizeData);
TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
pipe.GetOutputSchema(SchemaShape.Create(invalidDataWrongVectorSize.Schema));
try
{
pipe.Fit(invalidDataWrongVectorSize);
Assert.False(true);
}
catch (ArgumentOutOfRangeException) { }
catch (InvalidOperationException) { }
}