diff --git a/winml/test/api/LearningModelSessionAPITest.cpp b/winml/test/api/LearningModelSessionAPITest.cpp index 8e2a08ca72..8468891201 100644 --- a/winml/test/api/LearningModelSessionAPITest.cpp +++ b/winml/test/api/LearningModelSessionAPITest.cpp @@ -437,7 +437,7 @@ static void WindowFunction( auto double_data_type = TensorInt64Bit::CreateFromArray({}, {11}); auto window_operator = - Operator(window_operator_name, MS_EXPERIMENTAL_DOMAIN) + Operator(window_operator_name) .SetInput(L"size", L"Input") .SetOutput(L"output", L"Output"); @@ -446,7 +446,7 @@ static void WindowFunction( } auto model = - LearningModelBuilder::Create(13) + LearningModelBuilder::Create(17) .Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input", TensorKind::Int64, scalar_shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output", kind, output_shape)) .Operators().Add(window_operator) @@ -534,7 +534,7 @@ static void DiscreteFourierTransform_2D() { printf("\n Is Onesided: false"); auto builder = - LearningModelBuilder::Create(13) + LearningModelBuilder::Create(17) .Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input.Signal", TensorKind::Float, input_shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.Spectra", TensorKind::Float, output_shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.Inverse", TensorKind::Float, output_shape)) @@ -543,21 +543,23 @@ static void DiscreteFourierTransform_2D() { .SetInput(L"data", L"Input.Signal") .SetConstant(L"shape", TensorInt64Bit::CreateFromArray({4}, {INT64(1), INT64(height), INT64(width), INT64(1) })) .SetOutput(L"reshaped", L"reshaped_output")) - .Operators().Add(Operator(L"DFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"DFT") .SetInput(L"input", L"reshaped_output") .SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(1)})) .SetOutput(L"output", L"DFT.Output.1")) - .Operators().Add(Operator(L"DFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"DFT") .SetInput(L"input", L"DFT.Output.1") .SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(2)})) .SetOutput(L"output", L"DFT.Output.2")) - .Operators().Add(Operator(L"IDFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"DFT") .SetInput(L"input", L"DFT.Output.2") .SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(2)})) + .SetAttribute(L"inverse", TensorInt64Bit::CreateFromArray({}, {INT64(1)})) .SetOutput(L"output", L"IDFT.Output.1")) - .Operators().Add(Operator(L"IDFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"DFT") .SetInput(L"input", L"IDFT.Output.1") .SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(1)})) + .SetAttribute(L"inverse", TensorInt64Bit::CreateFromArray({}, {INT64(1)})) .SetOutput(L"output", L"IDFT.Output.2")) .Operators().Add(Operator(L"ReduceSumSquare") .SetInput(L"data", L"DFT.Output.2") @@ -664,11 +666,11 @@ static void DiscreteFourierTransform( printf("\n Is Onesided: %s", is_onesided ? "true" : "false"); auto model = - LearningModelBuilder::Create(13) + LearningModelBuilder::Create(17) .Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input.Signal", TensorKind::Float, input_shape)) .Inputs().AddConstant(L"Input.DFTLength", TensorInt64Bit::CreateFromArray({}, {INT64(dft_length)})) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.Spectra", TensorKind::Float, output_shape)) - .Operators().Add(Operator(L"DFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"DFT") .SetInput(L"input", L"Input.Signal") .SetInput(L"dft_length", L"Input.DFTLength") .SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(axis)})) @@ -768,14 +770,14 @@ static void STFT(size_t batch_size, size_t signal_size, size_t dft_size, auto dft_length = TensorInt64Bit::CreateFromArray({}, {INT64(dft_size)}); auto model = - LearningModelBuilder::Create(13) + LearningModelBuilder::Create(17) .Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input.TimeSignal", TensorKind::Float, input_shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.STFT", TensorKind::Float, output_shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.HannWindow", TensorKind::Float, {INT64(dft_size)})) - .Operators().Add(Operator(L"HannWindow", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"HannWindow") .SetConstant(L"size", dft_length) .SetOutput(L"output", L"Output.HannWindow")) - .Operators().Add(Operator(L"STFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"STFT") .SetAttribute(L"onesided", TensorInt64Bit::CreateFromArray({}, {INT64(is_onesided)})) .SetInput(L"signal", L"Input.TimeSignal") .SetInput(L"window", L"Output.HannWindow") @@ -833,9 +835,9 @@ static void ModelBuilding_MelWeightMatrix() { #if !defined(BUILD_INBOX) && defined(BUILD_MS_EXPERIMENTAL_OPS) std::vector output_shape = {INT64(9), INT64(8)}; auto builder = - LearningModelBuilder::Create(13) + LearningModelBuilder::Create(17) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.MelWeightMatrix", TensorKind::Float, output_shape)) - .Operators().Add(Operator(L"MelWeightMatrix", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"MelWeightMatrix") .SetConstant(L"num_mel_bins", TensorInt64Bit::CreateFromArray({}, {INT64(8)})) .SetConstant(L"dft_length", TensorInt64Bit::CreateFromArray({}, {INT64(16)})) .SetConstant(L"sample_rate", TensorInt64Bit::CreateFromArray({}, {INT64(8192)})) @@ -875,13 +877,13 @@ static void MelSpectrogramOnThreeToneSignal( std::vector mel_spectrogram_shape = {INT64(batch_size), 1, INT64(n_dfts), INT64(n_mel_bins)}; auto builder = - LearningModelBuilder::Create(13) + LearningModelBuilder::Create(17) .Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input.TimeSignal", TensorKind::Float, signal_shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.MelSpectrogram", TensorKind::Float, mel_spectrogram_shape)) - .Operators().Add(Operator(L"HannWindow", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"HannWindow") .SetConstant(L"size", TensorInt64Bit::CreateFromArray({}, {INT64(window_size)})) .SetOutput(L"output", L"hann_window")) - .Operators().Add(Operator(L"STFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"STFT") .SetName(L"STFT_NAMED_NODE") .SetInput(L"signal", L"Input.TimeSignal") .SetInput(L"window", L"hann_window") @@ -897,7 +899,7 @@ static void MelSpectrogramOnThreeToneSignal( .SetInput(L"A", L"magnitude_squared") .SetConstant(L"B", TensorFloat::CreateFromArray({}, {static_cast(dft_size)})) .SetOutput(L"C", L"power_frames")) - .Operators().Add(Operator(L"MelWeightMatrix", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"MelWeightMatrix") .SetConstant(L"num_mel_bins", TensorInt64Bit::CreateFromArray({}, {INT64(n_mel_bins)})) .SetConstant(L"dft_length", TensorInt64Bit::CreateFromArray({}, {INT64(dft_size)})) .SetConstant(L"sample_rate", TensorInt64Bit::CreateFromArray({}, {INT64(sampling_rate)})) @@ -1115,16 +1117,6 @@ static void ModelBuilding_ConstantMatmul() { static void ModelBuilding_DiscreteFourierTransform() { #if !defined(BUILD_INBOX) && defined(BUILD_MS_EXPERIMENTAL_OPS) - std::vector legacy_real_input = - { - 1.00f, 2.00, 3.00f, 4.00f, 5.00f, 6.00f, 7.00f, 8.00f, - }; - - std::vector> legacy_real_expected_axis_0_two_sided = { - {36.000f, 0.000f}, {-4.000f, 9.657f}, {-4.000f, 4.000f}, {-4.000f, 1.657f}, {-4.000f, 0.000f}, {-4.000f, -1.657f}, {-4.000f, -4.000f}, {-4.000f, -9.657f}, - }; - DiscreteFourierTransform(legacy_real_input, {1, 8}, legacy_real_expected_axis_0_two_sided, 1, 8, false /*onesided*/); - std::vector real_input = { 1.00f, 2.00, 3.00f, 4.00f, 5.00f, 6.00f, 7.00f, 8.00f, @@ -1263,15 +1255,15 @@ static void DiscreteFourierTransformInverse(size_t axis) { std::vector output_shape = {2, 5, 8, 2}; auto model = - LearningModelBuilder::Create(13) + LearningModelBuilder::Create(17) .Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input.TimeSignal", TensorKind::Float, shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.Spectra", TensorKind::Float, output_shape)) .Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.Inverse", TensorKind::Float, output_shape)) - .Operators().Add(Operator(L"DFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"DFT") .SetInput(L"input", L"Input.TimeSignal") .SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(axis)})) .SetOutput(L"output", L"Output.Spectra")) - .Operators().Add(Operator(L"DFT", MS_EXPERIMENTAL_DOMAIN) + .Operators().Add(Operator(L"DFT") .SetInput(L"input", L"Output.Spectra") .SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(axis)})) .SetAttribute(L"inverse", TensorInt64Bit::CreateFromArray({}, {INT64(1)}))