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LearningModelSessionAPITest.cpp
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LearningModelSessionAPITest.cpp
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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include "testPch.h"
#include "APITest.h"
#include "CommonDeviceHelpers.h"
#include "LearningModelSessionAPITest.h"
#include "protobufHelpers.h"
#include "winrt/Windows.Storage.h"
#include <D3d11_4.h>
#include <dxgi1_6.h>
#include "Psapi.h"
#include <complex>
using namespace winrt;
using namespace winml;
using namespace wfc;
#ifndef BUILD_INBOX
// experimental
using namespace winml_experimental;
using Operator = winml_experimental::LearningModelOperator;
static const wchar_t MS_EXPERIMENTAL_DOMAIN[] = L"com.microsoft.experimental";
#endif
using wf::IPropertyValue;
#define INT64(x) static_cast<int64_t>(x)
#define SIZET(x) static_cast<size_t>(x)
#define INT32(x) static_cast<int32_t>(x)
static void LearningModelSessionAPITestsClassSetup() {
init_apartment();
#ifdef BUILD_INBOX
winrt_activation_handler = WINRT_RoGetActivationFactory;
#endif
}
static void CreateSessionDeviceDefault() {
LearningModel learningModel = nullptr;
LearningModelDevice learningModelDevice = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
WINML_EXPECT_NO_THROW(learningModelDevice = LearningModelDevice(LearningModelDeviceKind::Default));
WINML_EXPECT_NO_THROW(LearningModelSession(learningModel, learningModelDevice));
}
static void CreateSessionDeviceCpu() {
LearningModel learningModel = nullptr;
LearningModelDevice learningModelDevice = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
WINML_EXPECT_NO_THROW(learningModelDevice = LearningModelDevice(LearningModelDeviceKind::Cpu));
WINML_EXPECT_NO_THROW(LearningModelSession(learningModel, learningModelDevice));
// for the CPU device, make sure that we get back NULL and 0 for any device properties
WINML_EXPECT_EQUAL(learningModelDevice.Direct3D11Device(), nullptr);
LARGE_INTEGER id;
id.QuadPart = APITest::GetAdapterIdQuadPart(learningModelDevice);
WINML_EXPECT_EQUAL(id.LowPart, static_cast<DWORD>(0));
WINML_EXPECT_EQUAL(id.HighPart, 0);
}
static void CreateSessionWithModelLoadedFromStream() {
LearningModel learningModel = nullptr;
LearningModelDevice learningModelDevice = nullptr;
std::wstring path = FileHelpers::GetModulePath() + L"model.onnx";
auto storageFile = ws::StorageFile::GetFileFromPathAsync(path).get();
WINML_EXPECT_NO_THROW(learningModel = LearningModel::LoadFromStream(storageFile));
WINML_EXPECT_NO_THROW(learningModelDevice = LearningModelDevice(LearningModelDeviceKind::Default));
WINML_EXPECT_NO_THROW(LearningModelSession(learningModel, learningModelDevice));
}
static void CreateSessionDeviceDirectX() {
LearningModel learningModel = nullptr;
LearningModelDevice learningModelDevice = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
WINML_EXPECT_NO_THROW(learningModelDevice = LearningModelDevice(LearningModelDeviceKind::DirectX));
WINML_EXPECT_NO_THROW(LearningModelSession(learningModel, learningModelDevice));
}
static void CreateSessionDeviceDirectXHighPerformance() {
LearningModel learningModel = nullptr;
LearningModelDevice learningModelDevice = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
WINML_EXPECT_NO_THROW(learningModelDevice = LearningModelDevice(LearningModelDeviceKind::DirectXHighPerformance));
WINML_EXPECT_NO_THROW(LearningModelSession(learningModel, learningModelDevice));
}
static void CreateSessionDeviceDirectXMinimumPower() {
LearningModel learningModel = nullptr;
LearningModelDevice learningModelDevice = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
WINML_EXPECT_NO_THROW(learningModelDevice = LearningModelDevice(LearningModelDeviceKind::DirectXMinPower));
WINML_EXPECT_NO_THROW(LearningModelSession(learningModel, learningModelDevice));
}
static void AdapterIdAndDevice() {
LearningModel learningModel = nullptr;
LearningModelDevice learningModelDevice = nullptr;
LearningModelSession learningModelSession = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
com_ptr<IDXGIFactory6> factory;
WINML_EXPECT_HRESULT_SUCCEEDED(CreateDXGIFactory1(__uuidof(IDXGIFactory6), factory.put_void()));
com_ptr<IDXGIAdapter> adapter;
learningModelDevice = LearningModelDevice(LearningModelDeviceKind::DirectX);
WINML_EXPECT_HRESULT_SUCCEEDED(factory->EnumAdapters(0, adapter.put()));
DXGI_ADAPTER_DESC desc;
WINML_EXPECT_HRESULT_SUCCEEDED(adapter->GetDesc(&desc));
LARGE_INTEGER id;
id.QuadPart = APITest::GetAdapterIdQuadPart(learningModelDevice);
WINML_EXPECT_EQUAL(desc.AdapterLuid.LowPart, id.LowPart);
WINML_EXPECT_EQUAL(desc.AdapterLuid.HighPart, id.HighPart);
WINML_EXPECT_TRUE(learningModelDevice.Direct3D11Device() != nullptr);
learningModelDevice = LearningModelDevice(LearningModelDeviceKind::DirectXHighPerformance);
adapter = nullptr;
WINML_EXPECT_HRESULT_SUCCEEDED(factory->EnumAdapterByGpuPreference(
0, DXGI_GPU_PREFERENCE_HIGH_PERFORMANCE, __uuidof(IDXGIAdapter), adapter.put_void()
));
WINML_EXPECT_HRESULT_SUCCEEDED(adapter->GetDesc(&desc));
id.QuadPart = APITest::GetAdapterIdQuadPart(learningModelDevice);
WINML_EXPECT_EQUAL(desc.AdapterLuid.LowPart, id.LowPart);
WINML_EXPECT_EQUAL(desc.AdapterLuid.HighPart, id.HighPart);
WINML_EXPECT_TRUE(learningModelDevice.Direct3D11Device() != nullptr);
adapter = nullptr;
learningModelDevice = LearningModelDevice(LearningModelDeviceKind::DirectXMinPower);
WINML_EXPECT_HRESULT_SUCCEEDED(factory->EnumAdapterByGpuPreference(
0, DXGI_GPU_PREFERENCE_MINIMUM_POWER, __uuidof(IDXGIAdapter), adapter.put_void()
));
WINML_EXPECT_HRESULT_SUCCEEDED(adapter->GetDesc(&desc));
id.QuadPart = APITest::GetAdapterIdQuadPart(learningModelDevice);
WINML_EXPECT_EQUAL(desc.AdapterLuid.LowPart, id.LowPart);
WINML_EXPECT_EQUAL(desc.AdapterLuid.HighPart, id.HighPart);
WINML_EXPECT_TRUE(learningModelDevice.Direct3D11Device() != nullptr);
WINML_EXPECT_NO_THROW(learningModelSession = LearningModelSession(learningModel, learningModelDevice));
WINML_EXPECT_EQUAL(learningModelSession.Device().AdapterId(), learningModelDevice.AdapterId());
}
static void EvaluateFeatures() {
std::vector<int64_t> shape = {4};
std::vector<winrt::hstring> data = {L"one", L"two", L"three", L"four"};
// create from buffer
auto tensor = TensorString::CreateFromArray(shape, data);
WINML_EXPECT_EQUAL(tensor.GetAsVectorView().Size(), data.size());
WINML_EXPECT_TRUE(std::equal(data.cbegin(), data.cend(), begin(tensor.GetAsVectorView())));
// create from vector view
auto dataCopy = data;
tensor = TensorString::CreateFromIterable(
shape, winrt::single_threaded_vector<winrt::hstring>(std::move(dataCopy)).GetView()
);
WINML_EXPECT_EQUAL(tensor.GetAsVectorView().Size(), data.size());
WINML_EXPECT_TRUE(std::equal(data.cbegin(), data.cend(), begin(tensor.GetAsVectorView())));
LearningModel learningModel = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"id-tensor-string.onnx", learningModel));
LearningModelSession session(learningModel);
auto outputTensor = TensorString::Create();
std::map<hstring, wf::IInspectable> featuresstandardmap;
featuresstandardmap[L"X"] = tensor;
featuresstandardmap[L"Y"] = outputTensor;
auto featureswinrtmap = winrt::single_threaded_map(std::move(featuresstandardmap));
session.EvaluateFeatures(featureswinrtmap, L"0");
// verify identity model round-trip works
WINML_EXPECT_EQUAL(outputTensor.GetAsVectorView().Size(), data.size());
WINML_EXPECT_TRUE(std::equal(data.cbegin(), data.cend(), begin(outputTensor.GetAsVectorView())));
}
static void EvaluateFeaturesAsync() {
std::vector<int64_t> shape = {4};
std::vector<winrt::hstring> data = {L"one", L"two", L"three", L"four"};
// create from buffer
auto tensor = TensorString::CreateFromArray(shape, data);
WINML_EXPECT_EQUAL(tensor.GetAsVectorView().Size(), data.size());
WINML_EXPECT_TRUE(std::equal(data.cbegin(), data.cend(), begin(tensor.GetAsVectorView())));
// create from vector view
auto dataCopy = data;
tensor = TensorString::CreateFromIterable(
shape, winrt::single_threaded_vector<winrt::hstring>(std::move(dataCopy)).GetView()
);
WINML_EXPECT_EQUAL(tensor.GetAsVectorView().Size(), data.size());
WINML_EXPECT_TRUE(std::equal(data.cbegin(), data.cend(), begin(tensor.GetAsVectorView())));
LearningModel learningModel = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"id-tensor-string.onnx", learningModel));
LearningModelSession session(learningModel);
auto outputTensor = TensorString::Create(shape);
std::map<hstring, wf::IInspectable> featuresstandardmap;
featuresstandardmap[L"X"] = tensor;
featuresstandardmap[L"Y"] = outputTensor;
auto featureswinrtmap = winrt::single_threaded_map(std::move(featuresstandardmap));
session.EvaluateFeaturesAsync(featureswinrtmap, L"0").get();
// verify identity model round-trip works
WINML_EXPECT_EQUAL(outputTensor.GetAsVectorView().Size(), data.size());
WINML_EXPECT_TRUE(std::equal(data.cbegin(), data.cend(), begin(outputTensor.GetAsVectorView())));
}
static void EvaluationProperties() {
// load a model
LearningModel learningModel = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
// create a session
LearningModelSession learningModelSession = nullptr;
learningModelSession = LearningModelSession(learningModel);
// set a property
auto value = winrt::Windows::Foundation::PropertyValue::CreateBoolean(true);
learningModelSession.EvaluationProperties().Insert(L"propName1", value);
// get the property and make sure it's there with the right value
auto value2 = learningModelSession.EvaluationProperties().Lookup(L"propName1");
WINML_EXPECT_EQUAL(value2.as<IPropertyValue>().GetBoolean(), true);
}
static LearningModelSession CreateSession(LearningModel model) {
LearningModelDevice device(nullptr);
WINML_EXPECT_NO_THROW(device = LearningModelDevice(LearningModelDeviceKind::DirectX));
LearningModelSession session(nullptr);
if (CommonDeviceHelpers::IsFloat16Supported(device)) {
WINML_EXPECT_NO_THROW(session = LearningModelSession(model, device));
} else {
WINML_EXPECT_THROW_SPECIFIC(
session = LearningModelSession(model, device),
winrt::hresult_error,
[](const winrt::hresult_error& e) -> bool { return e.code() == DXGI_ERROR_UNSUPPORTED; }
);
}
return session;
}
static void CreateSessionWithCastToFloat16InModel() {
// load a model
LearningModel learningModel = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"fp16-truncate-with-cast.onnx", learningModel));
CreateSession(learningModel);
}
static void CreateSessionWithFloat16InitializersInModel() {
// load a model
LearningModel learningModel = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"fp16-initializer.onnx", learningModel));
CreateSession(learningModel);
}
static void EvaluateSessionAndCloseModelHelper(LearningModelDeviceKind kind, bool close_model_on_session_creation) {
auto shape = std::vector<int64_t>{1, 1000};
auto model = ProtobufHelpers::CreateModel(TensorKind::Float, shape, 1000);
auto device = LearningModelDevice(kind);
auto options = LearningModelSessionOptions();
// close the model on session creation
options.CloseModelOnSessionCreation(close_model_on_session_creation);
// ensure you can create a session from the model
LearningModelSession session(nullptr);
WINML_EXPECT_NO_THROW(session = LearningModelSession(model, device, options));
std::vector<float> input(1000);
std::iota(std::begin(input), std::end(input), 0.0f);
auto tensor_input = TensorFloat::CreateFromArray(shape, input);
auto binding = LearningModelBinding(session);
binding.Bind(L"input", tensor_input);
LearningModelEvaluationResult result(nullptr);
WINML_EXPECT_NO_THROW(result = session.Evaluate(binding, L""));
if (close_model_on_session_creation) {
// ensure that the model has been closed
WINML_EXPECT_THROW_SPECIFIC(
LearningModelSession(model, device, options),
winrt::hresult_error,
[](const winrt::hresult_error& e) -> bool { return e.code() == E_INVALIDARG; }
);
} else {
WINML_EXPECT_NO_THROW(LearningModelSession(model, device, options));
}
}
static void EvaluateSessionAndCloseModel() {
WINML_EXPECT_NO_THROW(::EvaluateSessionAndCloseModelHelper(LearningModelDeviceKind::Cpu, true));
WINML_EXPECT_NO_THROW(::EvaluateSessionAndCloseModelHelper(LearningModelDeviceKind::Cpu, false));
}
static void NamedDimensionOverride() {
LearningModel model = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"fns-candy.onnx", model));
LearningModelDevice device(nullptr);
WINML_EXPECT_NO_THROW(device = LearningModelDevice(LearningModelDeviceKind::Cpu));
// the model input shape. the batch size, n, is overriden to 5
uint32_t n = 5;
int64_t c = 3, h = 720, w = 720;
LearningModelSessionOptions options;
options.OverrideNamedDimension(L"None", n);
// Verifies that if a Dim name doesn't exist the named dimension override does not interfere with successful evaluation
// The override is still expected to be present in the internal onnxruntime override data
options.OverrideNamedDimension(L"DimNameThatDoesntExist", n);
LearningModelSession session(nullptr);
WINML_EXPECT_NO_THROW(session = LearningModelSession(model, device, options));
#ifndef BUILD_INBOX
Experimental::LearningModelSessionExperimental experimental_session(session);
Experimental::LearningModelSessionOptionsExperimental experimental_options = experimental_session.Options();
wfc::IMapView<winrt::hstring, uint32_t> internal_overrides = experimental_options.GetNamedDimensionOverrides();
WINML_EXPECT_EQUAL(internal_overrides.Lookup(L"None"), n);
WINML_EXPECT_EQUAL(internal_overrides.Lookup(L"DimNameThatDoesntExist"), n);
#endif
ILearningModelFeatureDescriptor descriptor = model.InputFeatures().GetAt(0);
TensorFeatureDescriptor tensorDescriptor = nullptr;
descriptor.as(tensorDescriptor);
std::vector<int64_t> shape{n, c, h, w};
int64_t size = n * c * h * w;
std::vector<float> buffer;
buffer.resize(static_cast<size_t>(size));
auto featureValue = TensorFloat::CreateFromIterable(shape, winrt::single_threaded_vector<float>(std::move(buffer)));
LearningModelBinding binding(session);
binding.Bind(descriptor.Name(), featureValue);
WINML_EXPECT_NO_THROW(session.Evaluate(binding, L""));
}
static void CloseSession() {
LearningModel learningModel = nullptr;
WINML_EXPECT_NO_THROW(APITest::LoadModel(L"model.onnx", learningModel));
LearningModelSession session = nullptr;
/*
HANDLE currentProcessHandle = NULL;
try
{
currentProcessHandle = GetCurrentProcess();
}
catch (...)
{
VERIFY_FAIL(L"Failed to get current process handle.");
}
PROCESS_MEMORY_COUNTERS pmc = { 0 };
SIZE_T beforeSessionCloseWorkingSetSize = 0;
SIZE_T afterSessionCloseWorkingSetSize = 0;
bool getProcessMemoryInfoSuccess = false;
*/
WINML_EXPECT_NO_THROW(session = LearningModelSession(learningModel));
/*
// Get the current process memory info after session creation.
getProcessMemoryInfoSuccess = GetProcessMemoryInfo(currentProcessHandle, &pmc, sizeof(pmc));
if (!getProcessMemoryInfoSuccess)
{
VERIFY_FAIL(L"Failed to get current process memory info.");
}
beforeSessionCloseWorkingSetSize = pmc.WorkingSetSize;
pmc = { 0 };
*/
WINML_EXPECT_NO_THROW(session.Close());
/*
Bug 23659026: Working set difference tolerance is too tight for LearningModelSessionAPITests::CloseSession
https://microsoft.visualstudio.com/OS/_workitems/edit/23659026
// Check that working set size has dropped after session close
getProcessMemoryInfoSuccess = GetProcessMemoryInfo(currentProcessHandle, &pmc, sizeof(pmc));
if (!getProcessMemoryInfoSuccess)
{
VERIFY_FAIL(L"Failed to get current process memory info.");
}
afterSessionCloseWorkingSetSize = pmc.WorkingSetSize;
pmc = { 0 };
// expected working set difference of session close. It is approximately 2x the size of the weights of model.onnx
// there needs to be a tolerance because the working set difference varies from run to run.
// Bug 23739697: Closing Session API in LearningModelSessionAPITests::CloseSession doesn't always result in ~2x working set memory reduction.
// https://microsoft.visualstudio.com/OS/_workitems/edit/23739697
float tolerance = 0.4f;
int64_t expectedWorkingSetDifference = 9662464;
VERIFY_IS_LESS_THAN(expectedWorkingSetDifference - (beforeSessionCloseWorkingSetSize - afterSessionCloseWorkingSetSize), expectedWorkingSetDifference * tolerance);
*/
// verify that model still has metadata info after session close
std::wstring author(learningModel.Author());
WINML_EXPECT_EQUAL(author, L"onnx-caffe2");
// verify that session throws RO_E_CLOSED error
std::vector<float> input(1 * 3 * 224 * 224, 0);
std::vector<int64_t> shape = {1, 3, 224, 224};
auto tensor_input = TensorFloat::CreateFromArray(shape, input);
WINML_EXPECT_THROW_SPECIFIC(
LearningModelBinding binding(session),
winrt::hresult_error,
[](const winrt::hresult_error& e) -> bool { return e.code() == RO_E_CLOSED; }
);
}
#if !defined(BUILD_INBOX)
static void WindowFunction(const wchar_t* window_operator_name, TensorKind kind, const std::vector<float>& expected) {
std::vector<int64_t> scalar_shape = {};
std::vector<int64_t> output_shape = {32};
auto double_data_type = TensorInt64Bit::CreateFromArray({}, {11});
auto window_operator = Operator(window_operator_name).SetInput(L"size", L"Input").SetOutput(L"output", L"Output");
if (kind == TensorKind::Double) {
window_operator.SetAttribute(L"output_datatype", double_data_type);
}
auto model = 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)
.CreateModel();
LearningModelSession session(model);
LearningModelBinding binding(session);
binding.Bind(L"Input", TensorInt64Bit::CreateFromArray(scalar_shape, {32}));
// Evaluate
auto result = session.Evaluate(binding, L"");
// Check results
constexpr float error_threshold = .001f;
if (kind == TensorKind::Float) {
auto y_tensor = result.Outputs().Lookup(L"Output").as<TensorFloat>();
auto y_ivv = y_tensor.GetAsVectorView();
for (int i = 0; i < output_shape[0]; i++) {
WINML_EXPECT_TRUE(abs(y_ivv.GetAt(i) - expected[i]) < error_threshold);
}
}
if (kind == TensorKind::Double) {
auto y_tensor = result.Outputs().Lookup(L"Output").as<TensorDouble>();
auto y_ivv = y_tensor.GetAsVectorView();
for (int i = 0; i < output_shape[0]; i++) {
WINML_EXPECT_TRUE(abs(y_ivv.GetAt(i) - expected[i]) < error_threshold);
}
}
printf("\n");
}
#endif
static void SaveSoftwareBitmap(const wchar_t* filename, winrt::Windows::Graphics::Imaging::SoftwareBitmap bitmap) {
std::wstring modulePath = FileHelpers::GetModulePath();
winrt::Windows::Storage::StorageFolder folder =
winrt::Windows::Storage::StorageFolder::GetFolderFromPathAsync(modulePath).get();
winrt::Windows::Storage::StorageFile file =
folder.CreateFileAsync(filename, winrt::Windows::Storage::CreationCollisionOption::ReplaceExisting).get();
winrt::Windows::Storage::Streams::IRandomAccessStream write_stream =
file.OpenAsync(winrt::Windows::Storage::FileAccessMode::ReadWrite).get();
winrt::Windows::Graphics::Imaging::BitmapEncoder encoder =
winrt::Windows::Graphics::Imaging::BitmapEncoder::CreateAsync(
winrt::Windows::Graphics::Imaging::BitmapEncoder::JpegEncoderId(), write_stream
)
.get();
encoder.SetSoftwareBitmap(bitmap);
encoder.FlushAsync().get();
}
#if !defined(BUILD_INBOX)
static void DiscreteFourierTransform_2D(LearningModelDeviceKind kind) {
using namespace winrt::Windows::Storage;
using namespace winrt::Windows::Storage::Streams;
using namespace winrt::Windows::Graphics::Imaging;
using namespace winrt::Windows::Media;
std::wstring fullImagePath = FileHelpers::GetModulePath() + L"kitten_224.png";
winrt::Windows::Storage::StorageFile imagefile = StorageFile::GetFileFromPathAsync(fullImagePath).get();
IRandomAccessStream stream = imagefile.OpenAsync(FileAccessMode::Read).get();
SoftwareBitmap softwareBitmap = (BitmapDecoder::CreateAsync(stream).get()).GetSoftwareBitmapAsync().get();
VideoFrame frame = VideoFrame::CreateWithSoftwareBitmap(softwareBitmap);
auto corrected_image = winrt::Windows::Media::VideoFrame(
winrt::Windows::Graphics::Imaging::BitmapPixelFormat::Bgra8, INT32(256), INT32(256)
);
frame.CopyToAsync(corrected_image).get();
auto width = corrected_image.SoftwareBitmap().PixelWidth();
auto height = corrected_image.SoftwareBitmap().PixelHeight();
std::vector<int64_t> input_shape = {1, 1, height, width};
std::vector<int64_t> output_shape = {1, 1, height, width};
printf("N-Dimensional Discrete Fourier Transform");
printf("\n Input Shape: [");
for (size_t i = 0; i < input_shape.size(); i++) {
printf("%d,", static_cast<int>(input_shape[i]));
}
printf("]");
printf("\n Expected Output Shape: [");
for (size_t i = 0; i < output_shape.size(); i++) {
printf("%d,", static_cast<int>(output_shape[i]));
}
printf("]");
printf("\n Axis: [1,2]");
printf("\n Is Onesided: false");
auto builder =
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))
.Outputs()
.Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.Error", TensorKind::Float, output_shape))
.Operators()
.Add(Operator(L"Reshape")
.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")
.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")
.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"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"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")
.SetAttribute(L"axes", TensorInt64Bit::CreateFromArray({1}, {3}))
.SetAttribute(L"keepdims", TensorInt64Bit::CreateFromArray({}, {0}))
.SetOutput(L"reduced", L"magnitude_squared"))
.Operators()
.Add(Operator(L"Sqrt").SetInput(L"X", L"magnitude_squared").SetOutput(L"Y", L"sqrt_magnitude"))
.Operators()
.Add(Operator(L"ReduceSumSquare")
.SetInput(L"data", L"IDFT.Output.2")
.SetAttribute(L"axes", TensorInt64Bit::CreateFromArray({1}, {3}))
.SetAttribute(L"keepdims", TensorInt64Bit::CreateFromArray({}, {0}))
.SetOutput(L"reduced", L"magnitude_squared2"))
.Operators()
.Add(Operator(L"Sqrt").SetInput(L"X", L"magnitude_squared2").SetOutput(L"Y", L"sqrt_magnitude2"))
.Operators()
.Add(Operator(L"Reshape")
.SetInput(L"data", L"sqrt_magnitude")
.SetConstant(
L"shape", TensorInt64Bit::CreateFromArray({4}, {INT64(1), INT64(1), INT64(height), INT64(width)})
)
.SetOutput(L"reshaped", L"Output.Spectra"))
.Operators()
.Add(Operator(L"Reshape")
.SetInput(L"data", L"sqrt_magnitude2")
.SetConstant(
L"shape", TensorInt64Bit::CreateFromArray({4}, {INT64(1), INT64(1), INT64(height), INT64(width)})
)
.SetOutput(L"reshaped", L"Output.Inverse"))
.Operators()
.Add(Operator(L"Sub")
.SetInput(L"A", L"Input.Signal")
.SetInput(L"B", L"Output.Inverse")
.SetOutput(L"C", L"Output.Error"));
auto model = builder.CreateModel();
auto device = LearningModelDevice(kind);
LearningModelSession session(model, device);
LearningModelBinding binding(session);
// Bind input
binding.Bind(L"Input.Signal", frame);
// Bind output
auto spectra = VideoFrame(BitmapPixelFormat::Bgra8, INT32(width), INT32(height));
binding.Bind(L"Output.Spectra", spectra);
auto inverse = VideoFrame(BitmapPixelFormat::Bgra8, INT32(width), INT32(height));
binding.Bind(L"Output.Inverse", inverse);
// Evaluate
auto start = std::chrono::high_resolution_clock::now();
auto result = session.Evaluate(binding, L"");
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::micro> evaluate_duration_in_microseconds = end - start;
printf("\n Evaluate Took: %fus\n", evaluate_duration_in_microseconds.count());
auto error = result.Outputs().Lookup(L"Output.Error").as<TensorFloat>();
auto error_ivv = error.GetAsVectorView();
for (auto i = 0; i < height * width; i++) {
constexpr float error_threshold = .001f;
WINML_EXPECT_TRUE(abs(error_ivv.GetAt(i)) < error_threshold);
}
/*
* Output input, output and model
SaveSoftwareBitmap(L"fft2d.jpg", spectra.SoftwareBitmap());
SaveSoftwareBitmap(L"fft2d_inverse.jpg", inverse.SoftwareBitmap());
builder.Save(L"fft2d.onnx");
*/
printf("\n");
}
template <typename T>
static void DiscreteFourierTransform(
LearningModelDeviceKind kind,
const std::vector<T>& input,
const std::vector<int64_t>& input_shape,
const std::vector<std::complex<float>>& expected_output,
size_t axis,
size_t dft_length,
bool is_onesided = false
) {
// Calculate expected output shape
auto output_shape = input_shape;
if (output_shape.size() != 2) {
// If the input is not 2 dimensional, the last dimension is the complex component.
// DFT should always output complex results, and so we can comfortably coerce the last dim to 2
output_shape[output_shape.size() - 1] = 2;
} else {
// DFT should always output complex results. If input was 2 dimensional (real), we can comfortably append the last dim as 2
output_shape.push_back(2);
}
output_shape[axis] = is_onesided ? (1 + (dft_length >> 1)) : dft_length;
printf("Discrete Fourier Transform");
printf("\n Input Shape: [");
for (size_t i = 0; i < input_shape.size(); i++) {
printf("%d,", static_cast<int>(input_shape[i]));
}
printf("]");
printf("\n Expected Output Shape: [");
for (size_t i = 0; i < output_shape.size(); i++) {
printf("%d,", static_cast<int>(output_shape[i]));
}
printf("]");
printf("\n Axis: %d", static_cast<int>(axis));
printf("\n DFT Length: %d", static_cast<int>(dft_length));
printf("\n Is Onesided: %s", is_onesided ? "true" : "false");
auto model =
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")
.SetInput(L"input", L"Input.Signal")
.SetInput(L"dft_length", L"Input.DFTLength")
.SetAttribute(L"axis", TensorInt64Bit::CreateFromArray({}, {INT64(axis)}))
.SetAttribute(L"onesided", TensorInt64Bit::CreateFromArray({}, {is_onesided}))
.SetOutput(L"output", L"Output.Spectra"))
.CreateModel();
auto device = LearningModelDevice(kind);
LearningModelSession session(model, device);
LearningModelBinding binding(session);
auto is_real_input = input_shape.size() == 2 || input_shape[input_shape.size() - 1] == 1;
uint32_t input_stride = is_real_input ? 1 : 2;
// Populate binding
auto input_begin = const_cast<float*>(reinterpret_cast<const float*>(input.data()));
auto input_floats = winrt::array_view<float>(input_begin, static_cast<uint32_t>(input.size() * input_stride));
binding.Bind(L"Input.Signal", TensorFloat::CreateFromArray(input_shape, input_floats));
// Evaluate
auto start = std::chrono::high_resolution_clock::now();
auto result = session.Evaluate(binding, L"");
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::micro> evaluate_duration_in_microseconds = end - start;
printf("\n Evaluate Took: %fus", evaluate_duration_in_microseconds.count());
// Check results
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) {
// Check results
constexpr float error_threshold = .001f;
auto inRealRange = abs(y_ivv.GetAt(i) - expected_output[i / 2].real()) < error_threshold;
auto inImagRange = abs(y_ivv.GetAt(i + 1) - expected_output[i / 2].imag()) < error_threshold;
auto inRange = inRealRange && inImagRange;
if (!inRange) {
printf(
"[%d] ACTUAL(%f + %fi) EXPECTED(%f + %fi)\n",
(int)i / 2,
y_ivv.GetAt(i),
y_ivv.GetAt(i + 1),
expected_output[i / 2].real(),
expected_output[i / 2].imag()
);
}
WINML_EXPECT_TRUE(inRange);
}
printf("\n\n");
}
#endif
template <typename T>
static auto MakePureFrequency(float frequency_in_hertz, size_t signal_size, size_t sample_rate) {
float amplitude = 4;
float angular_velocity = frequency_in_hertz * 2 * static_cast<float>(M_PI);
std::vector<T> signal(signal_size);
for (size_t i = 0; i < signal_size; i++) {
T time = i / static_cast<T>(sample_rate);
signal[i] = amplitude * cos(angular_velocity * time);
}
return signal;
}
template <typename T>
static auto MakeMiddleC(size_t signal_size, size_t sample_rate) {
float middle_c_in_hertz = 261.626f;
return MakePureFrequency<T>(middle_c_in_hertz, signal_size, sample_rate);
}
template <typename T>
static auto MakeC2(size_t signal_size, size_t sample_rate) {
float middle_c_in_hertz = 261.626f * 2;
return MakePureFrequency<T>(middle_c_in_hertz, signal_size, sample_rate);
}
template <typename T>
static auto MakeC4(size_t signal_size, size_t sample_rate) {
float middle_c_in_hertz = 261.626f * 4;
return MakePureFrequency<T>(middle_c_in_hertz, signal_size, sample_rate);
}
template <typename T>
static auto MakeThreeTones(size_t signal_size, size_t sample_rate) {
auto middle_c = MakeMiddleC<T>(signal_size, sample_rate);
auto c2 = MakeC2<T>(signal_size, sample_rate);
auto c4 = MakeC4<T>(signal_size, sample_rate);
for (size_t i = 0; i < signal_size; i++) {
middle_c[i] = (i < signal_size / 3) ? middle_c[i]
: (i < 2 * signal_size / 3) ? (middle_c[i] + c2[i])
: (middle_c[i] + c2[i] + c4[i]);
}
return middle_c;
}
#if !defined(BUILD_INBOX)
static void STFT(
size_t batch_size, size_t signal_size, size_t dft_size, size_t hop_size, size_t sample_rate, bool is_onesided = false
) {
auto n_dfts = static_cast<size_t>(1 + floor((signal_size - dft_size) / hop_size));
auto input_shape = std::vector<int64_t>{1, INT64(signal_size)};
auto output_shape = std::vector<int64_t>{
INT64(batch_size), INT64(n_dfts), is_onesided ? ((INT64(dft_size) >> 1) + 1) : INT64(dft_size), 2
};
auto dft_length = TensorInt64Bit::CreateFromArray({}, {INT64(dft_size)});
auto model =
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").SetConstant(L"size", dft_length).SetOutput(L"output", L"Output.HannWindow"))
.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")
.SetConstant(L"frame_length", dft_length)
.SetConstant(L"frame_step", TensorInt64Bit::CreateFromArray({}, {INT64(hop_size)}))
.SetOutput(L"output", L"Output.STFT"))
.CreateModel();
LearningModelSession session(model);
LearningModelBinding binding(session);
// Create signal binding
auto signal = MakeMiddleC<float>(signal_size, sample_rate);
//printf("\n");
//printf("Input.TimeSignal:\n");
//for (size_t i = 0; i < dft_size; i++) {
// printf("%f, ", signal[i]);
//}
// Bind
binding.Bind(L"Input.TimeSignal", TensorFloat::CreateFromArray(input_shape, signal));
// Evaluate
auto result = session.Evaluate(binding, L"");
/*
printf("\n");
printf("Output.HannWindow\n");
auto window_tensor = result.Outputs().Lookup(L"Output.HannWindow").as<TensorFloat>();
auto window_ivv = window_tensor.GetAsVectorView();
for (uint32_t i = 0; i < window_ivv.Size(); i++) {
printf("%f, ", window_ivv.GetAt(i));
}
printf("\n");
printf("Output.STFT\n");
// Check results
auto y_tensor = result.Outputs().Lookup(L"Output.STFT").as<TensorFloat>();
auto y_ivv = y_tensor.GetAsVectorView();
auto size = y_ivv.Size();
WINML_EXPECT_EQUAL(size, n_dfts * output_shape[2] * 2);
for (size_t dft_idx = 0; dft_idx < n_dfts; dft_idx++) {
for (size_t i = 0; INT64(i) < output_shape[2]; i++) {
auto real_idx = static_cast<uint32_t>((i * 2) + (2 * dft_idx * output_shape[2]));
printf("(%d, %f , %fi), ", static_cast<uint32_t>(i), y_ivv.GetAt(real_idx), y_ivv.GetAt(real_idx + 1));
}
}
printf("\n");
*/
}
#endif
static void ModelBuilding_MelWeightMatrix() {
#if !defined(BUILD_INBOX)
std::vector<int64_t> output_shape = {INT64(9), INT64(8)};
auto builder = LearningModelBuilder::Create(17)
.Outputs()
.Add(LearningModelBuilder::CreateTensorFeatureDescriptor(
L"Output.MelWeightMatrix", TensorKind::Float, output_shape
))
.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)}))
.SetConstant(L"lower_edge_hertz", TensorFloat::CreateFromArray({}, {0}))
.SetConstant(L"upper_edge_hertz", TensorFloat::CreateFromArray({}, {8192 / 2.f}))
.SetOutput(L"output", L"Output.MelWeightMatrix"));
auto model = builder.CreateModel();
LearningModelSession session(model);
LearningModelBinding binding(session);
auto result = session.Evaluate(binding, L"");
/*
printf("\n");
printf("Output.MelWeightMatrix\n");
{
auto y_tensor = result.Outputs().Lookup(L"Output.MelWeightMatrix").as<TensorFloat>();
auto y_ivv = y_tensor.GetAsVectorView();
for (unsigned i = 0; i < y_ivv.Size(); i++) {
printf("%f, ", y_ivv.GetAt(i));
}
}
*/
printf("\n");
#endif
}
#if !defined(BUILD_INBOX)
static void MelSpectrogramOnThreeToneSignal(
size_t batch_size,
size_t signal_size,
size_t window_size,
size_t dft_size,
size_t hop_size,
size_t n_mel_bins,
size_t sampling_rate
) {
auto n_dfts = static_cast<size_t>(1 + floor((signal_size - dft_size) / hop_size));
auto onesided_dft_size = (dft_size >> 1) + 1;
std::vector<int64_t> signal_shape = {INT64(batch_size), INT64(signal_size)};
std::vector<int64_t> mel_spectrogram_shape = {INT64(batch_size), 1, INT64(n_dfts), INT64(n_mel_bins)};
auto builder =
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")
.SetConstant(L"size", TensorInt64Bit::CreateFromArray({}, {INT64(window_size)}))
.SetOutput(L"output", L"hann_window"))
.Operators()
.Add(Operator(L"STFT")
.SetName(L"STFT_NAMED_NODE")
.SetInput(L"signal", L"Input.TimeSignal")
.SetInput(L"window", L"hann_window")
.SetConstant(L"frame_length", TensorInt64Bit::CreateFromArray({}, {INT64(dft_size)}))
.SetConstant(L"frame_step", TensorInt64Bit::CreateFromArray({}, {INT64(hop_size)}))
.SetOutput(L"output", L"stft_output"))
.Operators()
.Add(Operator(L"ReduceSumSquare")
.SetInput(L"data", L"stft_output")
.SetAttribute(L"axes", TensorInt64Bit::CreateFromArray({1}, {3}))
.SetAttribute(L"keepdims", TensorInt64Bit::CreateFromArray({}, {0}))
.SetOutput(L"reduced", L"magnitude_squared"))
.Operators()
.Add(Operator(L"Div")
.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")
.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)}))
.SetConstant(L"lower_edge_hertz", TensorFloat::CreateFromArray({}, {0}))
.SetConstant(L"upper_edge_hertz", TensorFloat::CreateFromArray({}, {sampling_rate / 2.f}))
.SetOutput(L"output", L"mel_weight_matrix"))
.Operators()
.Add(Operator(L"Reshape")
.SetInput(L"data", L"power_frames")
.SetConstant(
L"shape", TensorInt64Bit::CreateFromArray({2}, {INT64(batch_size * n_dfts), INT64(onesided_dft_size)})
)
.SetOutput(L"reshaped", L"reshaped_output"))
.Operators()
.Add(Operator(L"MatMul")
.SetInput(L"A", L"reshaped_output")
.SetInput(L"B", L"mel_weight_matrix")
.SetOutput(L"Y", L"mel_spectrogram"))
.Operators()
.Add(Operator(L"Reshape")
.SetInput(L"data", L"mel_spectrogram")
.SetConstant(L"shape", TensorInt64Bit::CreateFromArray({4}, mel_spectrogram_shape))
.SetOutput(L"reshaped", L"Output.MelSpectrogram"));
auto model = builder.CreateModel();
LearningModelSession session(model);
LearningModelBinding binding(session);
// Bind input
auto signal = MakeThreeTones<float>(signal_size, sampling_rate);
binding.Bind(L"Input.TimeSignal", TensorFloat::CreateFromArray(signal_shape, signal));
// Bind output
auto output_image = winrt::Windows::Media::VideoFrame(
winrt::Windows::Graphics::Imaging::BitmapPixelFormat::Bgra8, INT32(n_mel_bins), INT32(n_dfts)
);
binding.Bind(L"Output.MelSpectrogram", output_image);
// Evaluate
auto start = std::chrono::high_resolution_clock::now();
auto result = session.Evaluate(binding, L"");
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::micro> evaluate_duration_in_microseconds = end - start;
printf("Evaluate Took: %fus\n", evaluate_duration_in_microseconds.count());
// Check the output video frame object by saving output image to disk
std::wstring out_name = L"mel_spectrogram.jpg";