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Fix WinML Tests are still targetting deprecated (deleted) experimental signal op definitions #12006

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Merged
merged 4 commits into from
Jun 27, 2022
Merged

Fix WinML Tests are still targetting deprecated (deleted) experimental signal op definitions #12006

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892d760
fix winml tests
smk2007 Jun 27, 2022
958f147
remove legacy test
smk2007 Jun 27, 2022
bde5686
switch idft -> dft+inverse attr
smk2007 Jun 27, 2022
cb9fe54
upgrade opset 13->!7 for signal ops tests
smk2007 Jun 27, 2022
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54 changes: 23 additions & 31 deletions winml/test/api/LearningModelSessionAPITest.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -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");

Expand All @@ -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)
Expand Down Expand Up @@ -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))
Expand All @@ -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")
Expand Down Expand Up @@ -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)}))
Expand Down Expand Up @@ -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")
Expand Down Expand Up @@ -833,9 +835,9 @@ static void ModelBuilding_MelWeightMatrix() {
#if !defined(BUILD_INBOX) && defined(BUILD_MS_EXPERIMENTAL_OPS)
std::vector<int64_t> 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)}))
Expand Down Expand Up @@ -875,13 +877,13 @@ static void MelSpectrogramOnThreeToneSignal(
std::vector<int64_t> 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")
Expand All @@ -897,7 +899,7 @@ static void MelSpectrogramOnThreeToneSignal(
.SetInput(L"A", L"magnitude_squared")
.SetConstant(L"B", TensorFloat::CreateFromArray({}, {static_cast<float>(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)}))
Expand Down Expand Up @@ -1115,16 +1117,6 @@ static void ModelBuilding_ConstantMatmul() {

static void ModelBuilding_DiscreteFourierTransform() {
#if !defined(BUILD_INBOX) && defined(BUILD_MS_EXPERIMENTAL_OPS)
std::vector<float> legacy_real_input =
{
1.00f, 2.00, 3.00f, 4.00f, 5.00f, 6.00f, 7.00f, 8.00f,
};

std::vector<std::complex<float>> 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<float> real_input =
{
1.00f, 2.00, 3.00f, 4.00f, 5.00f, 6.00f, 7.00f, 8.00f,
Expand Down Expand Up @@ -1263,15 +1255,15 @@ static void DiscreteFourierTransformInverse(size_t axis) {
std::vector<int64_t> 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)}))
Expand Down