void TestDnnImageFeaturizer()
{
// Onnxruntime supports Ubuntu 16.04, but not CentOS
// Do not execute on CentOS image
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
var samplevector = GetSampleArrayData();
var dataView = DataViewConstructionUtils.CreateFromList(Env,
new TestData[] {
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 DnnImageFeaturizerEstimator(Env, "output_1", m => m.ModelSelector.ResNet18(m.Environment, m.OutputColumn, m.InputColumn), "data_0");
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) { }
}
public void TestDnnImageFeaturizer()
{
//skip running for x86 as this test using too much memory (over 2GB limit on x86)
//and very like to hit memory related issue when running on CI
//TODO: optimized memory usage in related code and enable x86 test run
if (!Environment.Is64BitProcess)
{
return;
}
var samplevector = GetSampleArrayData();
var dataView = DataViewConstructionUtils.CreateFromList(Env,
new TestData[] {
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 = ML.Transforms.DnnFeaturizeImage("output_1", m => m.ModelSelector.ResNet18(m.Environment, m.OutputColumn, m.InputColumn), "data_0");
var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
var invalidDataWrongVectorSize = ML.Data.LoadFromEnumerable(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 TestDnnImageFeaturizer()
{
var samplevector = GetSampleArrayData();
var dataView = DataViewConstructionUtils.CreateFromList(Env,
new TestData[] {
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 = ML.Transforms.DnnFeaturizeImage("output_1", m => m.ModelSelector.ResNet18(m.Environment, m.OutputColumn, m.InputColumn), "data_0");
var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
var invalidDataWrongVectorSize = ML.Data.LoadFromEnumerable(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) { }
}