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fix complex multi-dim dft (#10896)

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Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
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Sheil Kumar 2022-03-17 12:45:51 -07:00 committed by GitHub
parent 07a71d5bf2
commit 810c18e809
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2 changed files with 60 additions and 15 deletions
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9
onnxruntime/contrib_ops/cpu/signal/dft.cc
View file

@ -278,17 +278,22 @@ static Status discrete_fourier_transform(OpKernelContext* ctx, const Tensor* X,
auto batch_and_signal_rank = X->Shape().NumDimensions();
auto total_dfts = static_cast<size_t>(X->Shape().Size() / X->Shape()[axis]);
auto is_input_real = X->Shape().NumDimensions() == 2 || X->Shape()[X->Shape().NumDimensions() - 1] == 1;
auto compex_input_factor = is_input_real ? 1 : 2;
if (X->Shape().NumDimensions() > 2)
{
total_dfts /= X->Shape()[X->Shape().NumDimensions() - 1];
batch_and_signal_rank -= 1;
}
// Calculate x/y offsets/strides
for (size_t i = 0; i < total_dfts; i++)
{
size_t X_offset = 0;
size_t X_stride = X_shape.SizeFromDimension(axis+1);
size_t X_stride = X_shape.SizeFromDimension(axis+1) / compex_input_factor;
size_t cumulative_packed_stride = total_dfts;
size_t temp = i;
for (size_t r = 0; r < batch_and_signal_rank; r++) {
@ -299,7 +304,7 @@ static Status discrete_fourier_transform(OpKernelContext* ctx, const Tensor* X,
cumulative_packed_stride /= X_shape[r];
auto index = temp / cumulative_packed_stride;
temp -= (index * cumulative_packed_stride);
X_offset += index * X_shape.SizeFromDimension(r + 1);
X_offset += index * X_shape.SizeFromDimension(r + 1) / compex_input_factor;
}
size_t Y_offset = 0;

66
winml/test/api/LearningModelSessionAPITest.cpp
View file

@ -944,40 +944,80 @@ static void ModelBuilding_DiscreteFourierTransform() {
#endif
}
static void ModelBuilding_DiscreteFourierTransformInverseIdentity() {
#if !defined(BUILD_INBOX) && defined(BUILD_MS_EXPERIMENTAL_OPS)
std::vector<int64_t> shape = {1, 5};
std::vector<int64_t> output_shape = {1, shape[1], 2};
static void DiscreteFourierTransformInverse(size_t axis) {
std::vector<int64_t> shape = {2, 5, 8, 1};
std::vector<int64_t> output_shape = {2, 5, 8, 2};
auto model =
LearningModelBuilder::Create(13)
.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)
.SetInput(L"input", L"Input.TimeSignal")
.SetOutput(L"output", L"DFTOutput"))
.Operators().Add(Operator(L"IDFT", MS_EXPERIMENTAL_DOMAIN)
.SetInput(L"input", L"DFTOutput")
.SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(axis)}))
.SetOutput(L"output", L"Output.Spectra"))
.Operators().Add(Operator(L"IDFT", MS_EXPERIMENTAL_DOMAIN)
.SetInput(L"input", L"Output.Spectra")
.SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(axis)}))
.SetOutput(L"output", L"Output.Inverse"))
.CreateModel();
LearningModelSession session(model);
LearningModelBinding binding(session);
auto input_vector =
std::vector<float>{
1, 2, 3, 4, 5, 6, 7, 8,
1, 2, 3, 4, 5, 6, 7, 8,
1, 2, 3, 4, 5, 6, 7, 8,
1, 2, 3, 4, 5, 6, 7, 8,
1, 2, 3, 4, 5, 6, 7, 8,
2, 4, 6, 8, 10, 12, 14, 16,
2, 4, 6, 8, 10, 12, 14, 16,
2, 4, 6, 8, 10, 12, 14, 16,
2, 4, 6, 8, 10, 12, 14, 16,
2, 4, 6, 8, 10, 12, 14, 16,
};
// Populate binding
binding.Bind(L"Input.TimeSignal", TensorFloat::CreateFromArray(shape, {1, 2, 3, 4, 5}));
binding.Bind(
L"Input.TimeSignal",
TensorFloat::CreateFromArray(
shape,
input_vector));
// Evaluate
auto result = session.Evaluate(binding, L"");
// Check results
printf("Output.Spectra\n");
auto y_tensor = result.Outputs().Lookup(L"Output.Spectra").as<TensorFloat>();
auto y_tensor = result.Outputs().Lookup(L"Output.Inverse").as<TensorFloat>();
auto y_ivv = y_tensor.GetAsVectorView();
for (int i = 0; i < output_shape[0] * output_shape[1] * 2; i += 2) {
printf("(%f + %fi), ", y_ivv.GetAt(i), y_ivv.GetAt(i + 1));
for (uint32_t i = 0; i < y_ivv.Size(); i += 2) {
WINML_EXPECT_TRUE(abs(y_ivv.GetAt(i) - input_vector[i / 2]) < .001);
WINML_EXPECT_TRUE(abs(y_ivv.GetAt(i + 1) - 0) < .001);
}
printf("\n");
//printf("Output.Spectra\n");
//auto y_tensor = result.Outputs().Lookup(L"Output.Spectra").as<TensorFloat>();
//auto y_ivv = y_tensor.GetAsVectorView();
//for (uint32_t i = 0; i < y_ivv.Size(); i+=2) {
// auto format_size = 16;
// if (i % format_size == 0 && i != 0) {
// printf("\n");
// }
// printf("(%.2f + %.2fi), ", y_ivv.GetAt(i), y_ivv.GetAt(i + 1));
//}
//printf("\n");
}
#endif
static void ModelBuilding_DiscreteFourierTransformInverseIdentity() {
#if !defined(BUILD_INBOX) && defined(BUILD_MS_EXPERIMENTAL_OPS)
DiscreteFourierTransformInverse(0);
DiscreteFourierTransformInverse(1);
#endif
}