[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) { } }