Clean up residual changes to squeezenet
This commit is contained in:
janezdu
Родитель
a7f3c3cfa6
Коммит
3f0a38fdb2
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
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@ -1,311 +0,0 @@
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#include "pch.h"
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#include "MelSpectrogram.h"
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#include "FileHelper.h"
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#include <winrt/Microsoft.AI.MachineLearning.Experimental.h>
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using namespace winrt;
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using namespace Microsoft::AI::MachineLearning::Experimental;
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using namespace Microsoft::AI::MachineLearning;
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static const wchar_t MS_EXPERIMENTAL_DOMAIN[] = L"com.microsoft.experimental";
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using Operator = LearningModelOperator;
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#define INT64(x) static_cast<int64_t>(x)
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#define SIZET(x) static_cast<size_t>(x)
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#define INT32(x) static_cast<int32_t>(x)
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template <typename T>
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static auto MakePureFrequency(float frequency_in_hertz, size_t signal_size, size_t sample_rate) {
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float amplitude = 4;
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float angular_velocity = frequency_in_hertz * 2 * 3.1415f;
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std::vector<T> signal(signal_size);
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for (size_t i = 0; i < signal_size; i++) {
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T time = i / static_cast<T>(sample_rate);
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signal[i] = amplitude * cos(angular_velocity * time);
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}
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return signal;
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}
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template <typename T>
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static auto MakeMiddleC(size_t signal_size, size_t sample_rate) {
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float middle_c_in_hertz = 261.626f;
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return MakePureFrequency<T>(middle_c_in_hertz, signal_size, sample_rate);
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}
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template <typename T>
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static auto MakeC2(size_t signal_size, size_t sample_rate) {
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float middle_c_in_hertz = 261.626f * 2;
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return MakePureFrequency<T>(middle_c_in_hertz, signal_size, sample_rate);
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}
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template <typename T>
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static auto MakeC4(size_t signal_size, size_t sample_rate) {
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float middle_c_in_hertz = 261.626f * 4;
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return MakePureFrequency<T>(middle_c_in_hertz, signal_size, sample_rate);
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}
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template <typename T>
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static auto MakeThreeTones(size_t signal_size, size_t sample_rate) {
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auto middle_c = MakeMiddleC<T>(signal_size, sample_rate);
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auto c2 = MakeC2<T>(signal_size, sample_rate);
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auto c4 = MakeC4<T>(signal_size, sample_rate);
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for (size_t i = 0; i < signal_size; i++) {
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middle_c[i] = (i < signal_size / 3) ?
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middle_c[i] :
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(i < 2 * signal_size / 3) ?
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(middle_c[i] + c2[i]) :
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(middle_c[i] + c2[i] + c4[i]);
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}
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return middle_c;
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}
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static void WindowFunction(const wchar_t* window_operator_name, TensorKind kind) {
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std::vector<int64_t> scalar_shape = {};
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std::vector<int64_t> output_shape = { 32 };
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auto double_data_type = TensorInt64Bit::CreateFromArray({}, { 11 });
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auto window_operator =
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Operator(window_operator_name, MS_EXPERIMENTAL_DOMAIN)
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.SetInput(L"size", L"Input")
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.SetOutput(L"output", L"Output");
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if (kind == TensorKind::Double) {
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window_operator.SetAttribute(L"output_datatype", double_data_type);
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}
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auto model =
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LearningModelBuilder::Create(13)
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.Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input", TensorKind::Int64, scalar_shape))
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.Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output", kind, output_shape))
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.Operators().Add(window_operator)
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.CreateModel();
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LearningModelSession session(model);
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LearningModelBinding binding(session);
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binding.Bind(L"Input", TensorInt64Bit::CreateFromArray(scalar_shape, { 32 }));
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// Evaluate
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auto result = session.Evaluate(binding, L"");
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// Check results
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printf("Output\n");
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if (kind == TensorKind::Float) {
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auto y_tensor = result.Outputs().Lookup(L"Output").as<TensorFloat>();
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auto y_ivv = y_tensor.GetAsVectorView();
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for (int i = 0; i < output_shape[0]; i++) {
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printf("%f, ", y_ivv.GetAt(i));
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}
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}
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if (kind == TensorKind::Double) {
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auto y_tensor = result.Outputs().Lookup(L"Output").as<TensorDouble>();
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auto y_ivv = y_tensor.GetAsVectorView();
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for (int i = 0; i < output_shape[0]; i++) {
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printf("%f, ", y_ivv.GetAt(i));
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}
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}
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printf("\n");
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}
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static void ModelBuilding_HannWindow() {
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WindowFunction(L"HannWindow", TensorKind::Float);
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WindowFunction(L"HannWindow", TensorKind::Double);
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}
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static void STFT(size_t batch_size, size_t signal_size, size_t dft_size,
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size_t hop_size, size_t sample_rate, bool is_onesided = false) {
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auto n_dfts = static_cast<size_t>(1 + floor((signal_size - dft_size) / hop_size));
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auto input_shape = std::vector<int64_t>{ 1, INT64(signal_size) };
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auto output_shape =
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std::vector<int64_t>{
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INT64(batch_size),
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INT64(n_dfts),
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is_onesided ? ((INT64(dft_size) >> 1) + 1) : INT64(dft_size),
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2
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};
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auto dft_length = TensorInt64Bit::CreateFromArray({}, { INT64(dft_size) });
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auto model =
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LearningModelBuilder::Create(13)
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.Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input.TimeSignal", TensorKind::Float, input_shape))
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.Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.STFT", TensorKind::Float, output_shape))
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.Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.HannWindow", TensorKind::Float, { INT64(dft_size) }))
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.Operators().Add(Operator(L"HannWindow", MS_EXPERIMENTAL_DOMAIN)
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.SetConstant(L"size", dft_length)
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.SetOutput(L"output", L"Output.HannWindow"))
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.Operators().Add(Operator(L"STFT", MS_EXPERIMENTAL_DOMAIN)
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.SetAttribute(L"onesided", TensorInt64Bit::CreateFromArray({}, { INT64(is_onesided) }))
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.SetInput(L"signal", L"Input.TimeSignal")
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.SetInput(L"window", L"Output.HannWindow")
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.SetConstant(L"frame_length", dft_length)
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.SetConstant(L"frame_step", TensorInt64Bit::CreateFromArray({}, { INT64(hop_size) }))
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.SetOutput(L"output", L"Output.STFT"))
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.CreateModel();
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LearningModelSession session(model);
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LearningModelBinding binding(session);
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// Create signal binding
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auto signal = MakeMiddleC<float>(signal_size, sample_rate);
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printf("\n");
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printf("Input.TimeSignal:\n");
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for (size_t i = 0; i < dft_size; i++) {
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printf("%f, ", signal[i]);
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}
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// Bind
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binding.Bind(L"Input.TimeSignal", TensorFloat::CreateFromArray(input_shape, signal));
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// Evaluate
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auto result = session.Evaluate(binding, L"");
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printf("\n");
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printf("Output.HannWindow\n");
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auto window_tensor = result.Outputs().Lookup(L"Output.HannWindow").as<TensorFloat>();
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auto window_ivv = window_tensor.GetAsVectorView();
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for (uint32_t i = 0; i < window_ivv.Size(); i++) {
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printf("%f, ", window_ivv.GetAt(i));
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}
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printf("\n");
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printf("Output.STFT\n");
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// Check results
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auto y_tensor = result.Outputs().Lookup(L"Output.STFT").as<TensorFloat>();
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auto y_ivv = y_tensor.GetAsVectorView();
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auto size = y_ivv.Size();
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//WINML_EXPECT_EQUAL(size, n_dfts * output_shape[2] * 2);
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for (size_t dft_idx = 0; dft_idx < n_dfts; dft_idx++) {
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for (size_t i = 0; INT64(i) < output_shape[2]; i++) {
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auto real_idx = static_cast<uint32_t>((i * 2) + (2 * dft_idx * output_shape[2]));
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printf("(%d, %f , %fi), ", static_cast<uint32_t>(i), y_ivv.GetAt(real_idx), y_ivv.GetAt(real_idx + 1));
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}
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}
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printf("\n");
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}
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static void ModelBuilding_MelWeightMatrix() {
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std::vector<int64_t> output_shape = { INT64(9), INT64(8) };
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auto builder =
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LearningModelBuilder::Create(13)
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.Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.MelWeightMatrix", TensorKind::Float, output_shape))
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.Operators().Add(Operator(L"MelWeightMatrix", MS_EXPERIMENTAL_DOMAIN)
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.SetConstant(L"num_mel_bins", TensorInt64Bit::CreateFromArray({}, { INT64(8) }))
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.SetConstant(L"dft_length", TensorInt64Bit::CreateFromArray({}, { INT64(16) }))
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.SetConstant(L"sample_rate", TensorInt64Bit::CreateFromArray({}, { INT64(8192) }))
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.SetConstant(L"lower_edge_hertz", TensorFloat::CreateFromArray({}, { 0 }))
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.SetConstant(L"upper_edge_hertz", TensorFloat::CreateFromArray({}, { 8192 / 2.f }))
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.SetOutput(L"output", L"Output.MelWeightMatrix"));
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auto model = builder.CreateModel();
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LearningModelSession session(model);
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LearningModelBinding binding(session);
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auto result = session.Evaluate(binding, L"");
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printf("\n");
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printf("Output.MelWeightMatrix\n");
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{
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auto y_tensor = result.Outputs().Lookup(L"Output.MelWeightMatrix").as<TensorFloat>();
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auto y_ivv = y_tensor.GetAsVectorView();
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for (unsigned i = 0; i < y_ivv.Size(); i++) {
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printf("%f, ", y_ivv.GetAt(i));
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}
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}
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printf("\n");
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}
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void MelSpectrogram::MelSpectrogramOnThreeToneSignal(
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size_t batch_size, size_t signal_size, size_t window_size, size_t dft_size,
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size_t hop_size, size_t n_mel_bins, size_t sampling_rate) {
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auto n_dfts = static_cast<size_t>(1 + floor((signal_size - dft_size) / hop_size));
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auto onesided_dft_size = (dft_size >> 1) + 1;
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std::vector<int64_t> signal_shape = { INT64(batch_size), INT64(signal_size) };
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std::vector<int64_t> mel_spectrogram_shape = { INT64(batch_size), 1, INT64(n_dfts), INT64(n_mel_bins) };
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auto builder =
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LearningModelBuilder::Create(13)
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.Inputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Input.TimeSignal", L"One-dimensional audio waveform", TensorKind::Float, signal_shape))
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.Outputs().Add(LearningModelBuilder::CreateTensorFeatureDescriptor(L"Output.MelSpectrogram", L"Output description", TensorKind::Float, mel_spectrogram_shape))
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.Operators().Add(Operator(L"HannWindow")
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.SetConstant(L"size", TensorInt64Bit::CreateFromArray({}, { INT64(window_size) }))
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.SetOutput(L"output", L"hann_window"))
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.Operators().Add(Operator(L"STFT")
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.SetName(L"STFT_NAMED_NODE")
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.SetInput(L"signal", L"Input.TimeSignal")
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.SetInput(L"window", L"hann_window")
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.SetConstant(L"frame_length", TensorInt64Bit::CreateFromArray({}, { INT64(dft_size) }))
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.SetConstant(L"frame_step", TensorInt64Bit::CreateFromArray({}, { INT64(hop_size) }))
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.SetOutput(L"output", L"stft_output"))
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.Operators().Add(Operator(L"ReduceSumSquare")
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.SetInput(L"data", L"stft_output")
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.SetAttribute(L"axes", TensorInt64Bit::CreateFromArray({ 1 }, { 3 }))
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.SetAttribute(L"keepdims", TensorInt64Bit::CreateFromArray({}, { 0 }))
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.SetOutput(L"reduced", L"magnitude_squared"))
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.Operators().Add(Operator(L"Div")
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.SetInput(L"A", L"magnitude_squared")
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.SetConstant(L"B", TensorFloat::CreateFromArray({}, { static_cast<float>(dft_size) }))
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.SetOutput(L"C", L"power_frames"))
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.Operators().Add(Operator(L"MelWeightMatrix")
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.SetConstant(L"num_mel_bins", TensorInt64Bit::CreateFromArray({}, { INT64(n_mel_bins) }))
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.SetConstant(L"dft_length", TensorInt64Bit::CreateFromArray({}, { INT64(dft_size) }))
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.SetConstant(L"sample_rate", TensorInt64Bit::CreateFromArray({}, { INT64(sampling_rate) }))
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.SetConstant(L"lower_edge_hertz", TensorFloat::CreateFromArray({}, { 0 }))
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.SetConstant(L"upper_edge_hertz", TensorFloat::CreateFromArray({}, { sampling_rate / 2.f }))
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.SetOutput(L"output", L"mel_weight_matrix"))
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.Operators().Add(Operator(L"Reshape")
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.SetInput(L"data", L"power_frames")
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.SetConstant(L"shape", TensorInt64Bit::CreateFromArray({ 2 }, { INT64(batch_size * n_dfts), INT64(onesided_dft_size) }))
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.SetOutput(L"reshaped", L"reshaped_output"))
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.Operators().Add(Operator(L"MatMul")
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.SetInput(L"A", L"reshaped_output")
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.SetInput(L"B", L"mel_weight_matrix")
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.SetOutput(L"Y", L"mel_spectrogram"))
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.Operators().Add(Operator(L"Reshape")
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.SetInput(L"data", L"mel_spectrogram")
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.SetConstant(L"shape", TensorInt64Bit::CreateFromArray({ 4 }, mel_spectrogram_shape))
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.SetOutput(L"reshaped", L"Output.MelSpectrogram"));
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auto model = builder.CreateModel();
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LearningModelSession session(model);
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LearningModelBinding binding(session);
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// Bind input
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auto signal = MakeThreeTones<float>(signal_size, sampling_rate);
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binding.Bind(L"Input.TimeSignal", TensorFloat::CreateFromArray(signal_shape, signal));
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// Bind output
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auto output_image =
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winrt::Windows::Media::VideoFrame(
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winrt::Windows::Graphics::Imaging::BitmapPixelFormat::Bgra8,
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INT32(n_mel_bins),
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INT32(n_dfts));
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binding.Bind(L"Output.MelSpectrogram", output_image);
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// Evaluate
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auto start = std::chrono::high_resolution_clock::now();
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auto result = session.Evaluate(binding, L"");
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auto end = std::chrono::high_resolution_clock::now();
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std::chrono::duration<double, std::micro> evaluate_duration_in_microseconds = end - start;
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printf("Evaluate Took: %f\n", evaluate_duration_in_microseconds.count());
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// Check the output video frame object by saving output image to disk
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std::wstring out_name = L"mel_spectrogram.jpg";
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// Save the output
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std::wstring modulePath = FileHelper::GetModulePath();
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winrt::Windows::Storage::StorageFolder folder = winrt::Windows::Storage::StorageFolder::GetFolderFromPathAsync(modulePath).get();
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winrt::Windows::Storage::StorageFile file = folder.CreateFileAsync(out_name, winrt::Windows::Storage::CreationCollisionOption::ReplaceExisting).get();
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winrt::Windows::Storage::Streams::IRandomAccessStream write_stream = file.OpenAsync(winrt::Windows::Storage::FileAccessMode::ReadWrite).get();
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winrt::Windows::Graphics::Imaging::BitmapEncoder encoder = winrt::Windows::Graphics::Imaging::BitmapEncoder::CreateAsync(winrt::Windows::Graphics::Imaging::BitmapEncoder::JpegEncoderId(), write_stream).get();
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encoder.SetSoftwareBitmap(output_image.SoftwareBitmap());
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encoder.FlushAsync().get();
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// Save the model
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builder.Save(L"spectrogram.onnx");
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printf("\n");
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}
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@ -1,11 +0,0 @@
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#pragma once
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#ifndef MELSPECTROGRAM_H
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#define MELSPECTROGRAM_H
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class MelSpectrogram {
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public:
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static void MelSpectrogramOnThreeToneSignal(
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size_t batch_size, size_t signal_size, size_t window_size, size_t dft_size,
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size_t hop_size, size_t n_mel_bins, size_t sampling_rate);
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};
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#endif
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@ -1,8 +1,7 @@
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<?xml version="1.0" encoding="utf-8"?>
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<Project DefaultTargets="Build" ToolsVersion="15.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
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<Import Project="..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.props" Condition="Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.props')" />
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<Import Project="..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.props" Condition="Exists('..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.props')" />
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<Import Project="..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.props" Condition="Exists('..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.props')" />
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<Import Project="..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.props" Condition="Exists('..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.props')" />
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<Import Project="..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.props" Condition="Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.props')" />
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<PropertyGroup Label="Globals">
|
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<CppWinRTEnabled>true</CppWinRTEnabled>
|
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<RequiredBundles>$(RequiredBundles);Microsoft.Windows.CppWinRT</RequiredBundles>
|
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@ -11,9 +10,9 @@
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<ProjectGuid>{2bf804d4-daa2-42be-9f21-0e94f021ef53}</ProjectGuid>
|
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<Keyword>Win32Proj</Keyword>
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<RootNamespace>SqueezeNetObjectDetection</RootNamespace>
|
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<WindowsTargetPlatformVersion Condition=" '$(WindowsTargetPlatformVersion)' == '' ">10.0</WindowsTargetPlatformVersion>
|
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<WindowsTargetPlatformVersion Condition=" '$(WindowsTargetPlatformVersion)' == '' ">10.0.18362.0</WindowsTargetPlatformVersion>
|
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<WindowsTargetPlatformMinVersion>10.0.17763.0</WindowsTargetPlatformMinVersion>
|
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<ProjectName>MelSpectrogram</ProjectName>
|
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<ProjectName>SqueezeNetObjectDetectionCPP</ProjectName>
|
||||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
|
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<ItemGroup Label="ProjectConfigurations">
|
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@ -44,7 +43,7 @@
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</ItemGroup>
|
||||
<PropertyGroup Label="Configuration">
|
||||
<ConfigurationType>Application</ConfigurationType>
|
||||
<PlatformToolset>v142</PlatformToolset>
|
||||
<PlatformToolset>v141</PlatformToolset>
|
||||
<CharacterSet>Unicode</CharacterSet>
|
||||
</PropertyGroup>
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||||
<PropertyGroup Condition="'$(Configuration)'=='Debug'" Label="Configuration">
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@ -124,11 +123,9 @@
|
|||
</Link>
|
||||
</ItemDefinitionGroup>
|
||||
<ItemGroup>
|
||||
<ClInclude Include="MelSpectrogram.h" />
|
||||
<ClInclude Include="pch.h" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<ClCompile Include="MelSpectrogram.cpp" />
|
||||
<ClCompile Include="main.cpp" />
|
||||
<ClCompile Include="pch.cpp">
|
||||
<PrecompiledHeader>Create</PrecompiledHeader>
|
||||
|
|
@ -161,6 +158,7 @@
|
|||
<ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|ARM'">false</ExcludedFromBuild>
|
||||
<FileType>Document</FileType>
|
||||
</CopyFileToFolders>
|
||||
<None Include="packages.config" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<CopyFileToFolders Include="..\..\..\..\SharedContent\media\kitten_224.png">
|
||||
|
|
@ -183,15 +181,11 @@
|
|||
<Project>{12103a5b-677a-4286-83d2-54eab9010c16}</Project>
|
||||
</ProjectReference>
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<None Include="packages.config" />
|
||||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(SolutionDir)\packages\rapidjson.v110.1.1.0\build\native\rapidjson.targets" Condition="Exists('$(SolutionDir)\packages\rapidjson.v110.1.1.0\build\native\rapidjson.targets')" />
|
||||
<Import Project="..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.targets" Condition="Exists('..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.targets')" />
|
||||
<Import Project="..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.targets" Condition="Exists('..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.targets')" />
|
||||
<Import Project="..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.targets" Condition="Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.targets')" />
|
||||
<Import Project="..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.targets" Condition="Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.targets')" />
|
||||
<Import Project="..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.targets" Condition="Exists('..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.targets')" />
|
||||
</ImportGroup>
|
||||
<ItemDefinitionGroup>
|
||||
<ClCompile>
|
||||
|
|
@ -219,11 +213,9 @@
|
|||
<PropertyGroup>
|
||||
<ErrorText>This project references NuGet package(s) that are missing on this computer. Use NuGet Package Restore to download them. For more information, see http://go.microsoft.com/fwlink/?LinkID=322105. The missing file is {0}.</ErrorText>
|
||||
</PropertyGroup>
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.props')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.props'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.AI.DirectML.1.5.1\build\Microsoft.AI.DirectML.targets'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.props')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.props'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.AI.MachineLearning.1.8.0\build\native\Microsoft.AI.MachineLearning.targets'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.props')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.props'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.Windows.CppWinRT.2.0.210505.3\build\native\Microsoft.Windows.CppWinRT.targets'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.props')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.props'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.Windows.CppWinRT.2.0.200316.3\build\native\Microsoft.Windows.CppWinRT.targets'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.props')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.props'))" />
|
||||
<Error Condition="!Exists('..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\..\packages\Microsoft.AI.MachineLearning.1.3.0\build\native\Microsoft.AI.MachineLearning.targets'))" />
|
||||
</Target>
|
||||
</Project>
|
||||
|
|
@ -3,11 +3,12 @@
|
|||
<ItemGroup>
|
||||
<ClCompile Include="main.cpp" />
|
||||
<ClCompile Include="pch.cpp" />
|
||||
<ClCompile Include="MelSpectrogram.cpp" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<ClInclude Include="pch.h" />
|
||||
<ClInclude Include="MelSpectrogram.h" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<None Include="packages.config" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<Filter Include="SharedContent">
|
||||
|
|
@ -25,7 +26,4 @@
|
|||
<Filter>SharedContent</Filter>
|
||||
</CopyFileToFolders>
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<None Include="packages.config" />
|
||||
</ItemGroup>
|
||||
</Project>
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
#include "pch.h"
|
||||
#include "backup.h"
|
||||
#if __has_include("backup.g.cpp")
|
||||
#include "backup.g.cpp"
|
||||
#endif
|
||||
|
||||
using namespace winrt;
|
||||
using namespace Windows::UI::Xaml;
|
||||
|
||||
namespace winrt::SqueezeNetObjectDetection::implementation
|
||||
{
|
||||
backup::backup()
|
||||
{
|
||||
InitializeComponent();
|
||||
}
|
||||
|
||||
int32_t backup::MyProperty()
|
||||
{
|
||||
throw hresult_not_implemented();
|
||||
}
|
||||
|
||||
void backup::MyProperty(int32_t /* value */)
|
||||
{
|
||||
throw hresult_not_implemented();
|
||||
}
|
||||
|
||||
void backup::ClickHandler(IInspectable const&, RoutedEventArgs const&)
|
||||
{
|
||||
Button().Content(box_value(L"Clicked"));
|
||||
}
|
||||
}
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
#include "backup.g.h"
|
||||
|
||||
namespace winrt::SqueezeNetObjectDetection::implementation
|
||||
{
|
||||
struct backup : backupT<backup>
|
||||
{
|
||||
backup();
|
||||
|
||||
int32_t MyProperty();
|
||||
void MyProperty(int32_t value);
|
||||
|
||||
void ClickHandler(Windows::Foundation::IInspectable const& sender, Windows::UI::Xaml::RoutedEventArgs const& args);
|
||||
};
|
||||
}
|
||||
|
||||
namespace winrt::SqueezeNetObjectDetection::factory_implementation
|
||||
{
|
||||
struct backup : backupT<backup, implementation::backup>
|
||||
{
|
||||
};
|
||||
}
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
namespace SqueezeNetObjectDetection
|
||||
{
|
||||
[default_interface]
|
||||
runtimeclass backup : Windows.UI.Xaml.Controls.Page
|
||||
{
|
||||
backup();
|
||||
Int32 MyProperty;
|
||||
}
|
||||
}
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
<Page
|
||||
x:Class="SqueezeNetObjectDetection.backup"
|
||||
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
|
||||
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
|
||||
xmlns:local="using:SqueezeNetObjectDetection"
|
||||
xmlns:d="http://schemas.microsoft.com/expression/blend/2008"
|
||||
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
|
||||
mc:Ignorable="d">
|
||||
|
||||
<StackPanel Orientation="Horizontal" HorizontalAlignment="Center" VerticalAlignment="Center">
|
||||
<Button x:Name="Button" Click="ClickHandler">Click Me</Button>
|
||||
</StackPanel>
|
||||
</Page>
|
||||
|
|
@ -3,22 +3,20 @@
|
|||
|
||||
#include "pch.h"
|
||||
#include "FileHelper.h"
|
||||
#include <winrt/Microsoft.AI.MachineLearning.Experimental.h>
|
||||
#include "MelSpectrogram.h"
|
||||
|
||||
using namespace winrt;
|
||||
using namespace Windows::Foundation;
|
||||
using namespace Windows::Foundation::Collections;
|
||||
|
||||
#ifdef USE_WINML_NUGET
|
||||
using namespace Microsoft::AI::MachineLearning;
|
||||
|
||||
#else
|
||||
using namespace Windows::AI::MachineLearning;
|
||||
#endif
|
||||
using namespace Windows::Media;
|
||||
using namespace Windows::Graphics::Imaging;
|
||||
using namespace Windows::Storage;
|
||||
using namespace std;
|
||||
|
||||
|
||||
|
||||
// globals
|
||||
vector<string> labels;
|
||||
string labelsFileName("labels.txt");
|
||||
|
|
@ -43,149 +41,178 @@ wstring GetModelPath()
|
|||
// usage: SqueezeNet [imagefile] [cpu|directx]
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
printf("Melspec on 3 tone signal \n");
|
||||
init_apartment();
|
||||
|
||||
MelSpectrogram ms;
|
||||
// did they pass in the args
|
||||
if (ParseArgs(argc, argv) == false)
|
||||
{
|
||||
printf("Usage: %s [imagefile] [cpu|directx]", argv[0]);
|
||||
return -1;
|
||||
}
|
||||
|
||||
size_t batch_size = 1;
|
||||
size_t signal_size = 40;
|
||||
size_t window_size = 5;
|
||||
size_t dft_size = 3;
|
||||
size_t hop_size = 1;
|
||||
size_t n_mel_bins = 32;
|
||||
size_t sampling_rate = 4000;
|
||||
// Get model path
|
||||
auto modelPath = GetModelPath();
|
||||
|
||||
// load the model
|
||||
printf("Loading modelfile '%ws' on the '%s' device\n", modelPath.c_str(), deviceName.c_str());
|
||||
DWORD ticks = GetTickCount();
|
||||
auto model = LearningModel::LoadFromFilePath(modelPath);
|
||||
ticks = GetTickCount() - ticks;
|
||||
printf("model file loaded in %d ticks\n", ticks);
|
||||
|
||||
ms.MelSpectrogramOnThreeToneSignal(batch_size, signal_size, window_size, dft_size,
|
||||
hop_size, n_mel_bins, sampling_rate);
|
||||
printf("done");
|
||||
|
||||
// get model color management mode
|
||||
printf("Getting model color management mode...\n");
|
||||
ColorManagementMode colorManagementMode = GetColorManagementMode(model);
|
||||
|
||||
// load the image
|
||||
printf("Loading the image...\n");
|
||||
auto imageFrame = LoadImageFile(imagePath, colorManagementMode);
|
||||
// now create a session and binding
|
||||
LearningModelSession session(model, LearningModelDevice(deviceKind));
|
||||
LearningModelBinding binding(session);
|
||||
|
||||
// bind the intput image
|
||||
printf("Binding...\n");
|
||||
binding.Bind(L"data_0", ImageFeatureValue::CreateFromVideoFrame(imageFrame));
|
||||
// temp: bind the output (we don't support unbound outputs yet)
|
||||
vector<int64_t> shape({ 1, 1000, 1, 1 });
|
||||
binding.Bind(L"softmaxout_1", TensorFloat::Create(shape));
|
||||
|
||||
// now run the model
|
||||
printf("Running the model...\n");
|
||||
ticks = GetTickCount();
|
||||
auto results = session.Evaluate(binding, L"RunId");
|
||||
ticks = GetTickCount() - ticks;
|
||||
printf("model run took %d ticks\n", ticks);
|
||||
|
||||
// get the output
|
||||
auto resultTensor = results.Outputs().Lookup(L"softmaxout_1").as<TensorFloat>();
|
||||
auto resultVector = resultTensor.GetAsVectorView();
|
||||
PrintResults(resultVector);
|
||||
}
|
||||
|
||||
bool ParseArgs(int argc, char* argv[])
|
||||
{
|
||||
if (argc < 2)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
// get the image file
|
||||
imagePath = hstring(wstring_to_utf8().from_bytes(argv[1]));
|
||||
// did they pass a third arg?
|
||||
if (argc >= 3)
|
||||
{
|
||||
deviceName = argv[2];
|
||||
if (deviceName == "cpu")
|
||||
{
|
||||
deviceKind = LearningModelDeviceKind::Cpu;
|
||||
}
|
||||
else if (deviceName == "directx")
|
||||
{
|
||||
deviceKind = LearningModelDeviceKind::DirectX;
|
||||
}
|
||||
else
|
||||
{
|
||||
deviceName = "default";
|
||||
deviceKind = LearningModelDeviceKind::Default;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
ColorManagementMode GetColorManagementMode(const LearningModel& model)
|
||||
{
|
||||
// Get model color space gamma
|
||||
hstring gammaSpace = L"";
|
||||
try
|
||||
{
|
||||
gammaSpace = model.Metadata().Lookup(L"Image.ColorSpaceGamma");
|
||||
}
|
||||
catch (...)
|
||||
{
|
||||
printf(" Model does not have color space gamma information. Will color manage to sRGB by default...\n");
|
||||
}
|
||||
if (gammaSpace == L"" || _wcsicmp(gammaSpace.c_str(), L"SRGB") == 0)
|
||||
{
|
||||
return ColorManagementMode::ColorManageToSRgb;
|
||||
}
|
||||
// Due diligence should be done to make sure that the input image is within the model's colorspace. There are multiple non-sRGB color spaces.
|
||||
printf(" Model metadata indicates that color gamma space is : %ws. Will not manage color space to sRGB...\n", gammaSpace.c_str());
|
||||
return ColorManagementMode::DoNotColorManage;
|
||||
}
|
||||
|
||||
//bool ParseArgs(int argc, char* argv[])
|
||||
//{
|
||||
// if (argc < 2)
|
||||
// {
|
||||
// return false;
|
||||
// }
|
||||
// // get the image file
|
||||
// imagePath = hstring(wstring_to_utf8().from_bytes(argv[1]));
|
||||
// // did they pass a third arg?
|
||||
// if (argc >= 3)
|
||||
// {
|
||||
// deviceName = argv[2];
|
||||
// if (deviceName == "cpu")
|
||||
// {
|
||||
// deviceKind = LearningModelDeviceKind::Cpu;
|
||||
// }
|
||||
// else if (deviceName == "directx")
|
||||
// {
|
||||
// deviceKind = LearningModelDeviceKind::DirectX;
|
||||
// }
|
||||
// else
|
||||
// {
|
||||
// deviceName = "default";
|
||||
// deviceKind = LearningModelDeviceKind::Default;
|
||||
// }
|
||||
// }
|
||||
// return true;
|
||||
//}
|
||||
//
|
||||
//ColorManagementMode GetColorManagementMode(const LearningModel& model)
|
||||
//{
|
||||
// // Get model color space gamma
|
||||
// hstring gammaSpace = L"";
|
||||
// try
|
||||
// {
|
||||
// gammaSpace = model.Metadata().Lookup(L"Image.ColorSpaceGamma");
|
||||
// }
|
||||
// catch (...)
|
||||
// {
|
||||
// printf(" Model does not have color space gamma information. Will color manage to sRGB by default...\n");
|
||||
// }
|
||||
// if (gammaSpace == L"" || _wcsicmp(gammaSpace.c_str(), L"SRGB") == 0)
|
||||
// {
|
||||
// return ColorManagementMode::ColorManageToSRgb;
|
||||
// }
|
||||
// // Due diligence should be done to make sure that the input image is within the model's colorspace. There are multiple non-sRGB color spaces.
|
||||
// printf(" Model metadata indicates that color gamma space is : %ws. Will not manage color space to sRGB...\n", gammaSpace.c_str());
|
||||
// return ColorManagementMode::DoNotColorManage;
|
||||
//}
|
||||
//
|
||||
//VideoFrame LoadImageFile(hstring filePath, ColorManagementMode colorManagementMode)
|
||||
//{
|
||||
// BitmapDecoder decoder = NULL;
|
||||
// try
|
||||
// {
|
||||
// // open the file
|
||||
// StorageFile file = StorageFile::GetFileFromPathAsync(filePath).get();
|
||||
// // get a stream on it
|
||||
// auto stream = file.OpenAsync(FileAccessMode::Read).get();
|
||||
// // Create the decoder from the stream
|
||||
// decoder = BitmapDecoder::CreateAsync(stream).get();
|
||||
// }
|
||||
// catch (...)
|
||||
// {
|
||||
// printf(" Failed to load the image file, make sure you are using fully qualified paths\r\n");
|
||||
// exit(EXIT_FAILURE);
|
||||
// }
|
||||
// SoftwareBitmap softwareBitmap = NULL;
|
||||
// try
|
||||
// {
|
||||
// softwareBitmap = decoder.GetSoftwareBitmapAsync(
|
||||
// decoder.BitmapPixelFormat(),
|
||||
// decoder.BitmapAlphaMode(),
|
||||
// BitmapTransform(),
|
||||
// ExifOrientationMode::RespectExifOrientation,
|
||||
// colorManagementMode
|
||||
// ).get();
|
||||
// }
|
||||
// catch (hresult_error hr)
|
||||
// {
|
||||
// printf(" Failed to create SoftwareBitmap! Please make sure that input image is within the model's colorspace.\n");
|
||||
// printf(" %ws\n", hr.message().c_str());
|
||||
// exit(hr.code());
|
||||
// }
|
||||
// VideoFrame inputImage = NULL;
|
||||
// try
|
||||
// {
|
||||
// // load a videoframe from it
|
||||
// inputImage = VideoFrame::CreateWithSoftwareBitmap(softwareBitmap);
|
||||
// }
|
||||
// catch (hresult_error hr)
|
||||
// {
|
||||
// printf("Failed to create videoframe from software bitmap.");
|
||||
// printf(" %ws\n", hr.message().c_str());
|
||||
// exit(hr.code());
|
||||
// }
|
||||
// // all done
|
||||
// return inputImage;
|
||||
//}
|
||||
//
|
||||
//void PrintResults(IVectorView<float> results)
|
||||
//{
|
||||
// // load the labels
|
||||
// auto modulePath = FileHelper::GetModulePath();
|
||||
// std::string labelsFilePath =
|
||||
// std::string(modulePath.begin(), modulePath.end()) + labelsFileName;
|
||||
// labels = FileHelper::LoadLabels(labelsFilePath);
|
||||
//
|
||||
// vector<pair<float, uint32_t>> sortedResults;
|
||||
// for (uint32_t i = 0; i < results.Size(); i++) {
|
||||
// pair<float, uint32_t> curr;
|
||||
// curr.first = results.GetAt(i);
|
||||
// curr.second = i;
|
||||
// sortedResults.push_back(curr);
|
||||
// }
|
||||
// std::sort(sortedResults.begin(), sortedResults.end(),
|
||||
// [](pair<float, uint32_t> const &a, pair<float, uint32_t> const &b) { return a.first > b.first; });
|
||||
//
|
||||
// // Display the result
|
||||
// for (int i = 0; i < 3; i++)
|
||||
// {
|
||||
// pair<float, uint32_t> curr = sortedResults.at(i);
|
||||
// printf("%s with confidence of %f\n", labels[curr.second].c_str(), curr.first);
|
||||
// }
|
||||
//}
|
||||
VideoFrame LoadImageFile(hstring filePath, ColorManagementMode colorManagementMode)
|
||||
{
|
||||
BitmapDecoder decoder = NULL;
|
||||
try
|
||||
{
|
||||
// open the file
|
||||
StorageFile file = StorageFile::GetFileFromPathAsync(filePath).get();
|
||||
// get a stream on it
|
||||
auto stream = file.OpenAsync(FileAccessMode::Read).get();
|
||||
// Create the decoder from the stream
|
||||
decoder = BitmapDecoder::CreateAsync(stream).get();
|
||||
}
|
||||
catch (...)
|
||||
{
|
||||
printf(" Failed to load the image file, make sure you are using fully qualified paths\r\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
SoftwareBitmap softwareBitmap = NULL;
|
||||
try
|
||||
{
|
||||
softwareBitmap = decoder.GetSoftwareBitmapAsync(
|
||||
decoder.BitmapPixelFormat(),
|
||||
decoder.BitmapAlphaMode(),
|
||||
BitmapTransform(),
|
||||
ExifOrientationMode::RespectExifOrientation,
|
||||
colorManagementMode
|
||||
).get();
|
||||
}
|
||||
catch (hresult_error hr)
|
||||
{
|
||||
printf(" Failed to create SoftwareBitmap! Please make sure that input image is within the model's colorspace.\n");
|
||||
printf(" %ws\n", hr.message().c_str());
|
||||
exit(hr.code());
|
||||
}
|
||||
VideoFrame inputImage = NULL;
|
||||
try
|
||||
{
|
||||
// load a videoframe from it
|
||||
inputImage = VideoFrame::CreateWithSoftwareBitmap(softwareBitmap);
|
||||
}
|
||||
catch (hresult_error hr)
|
||||
{
|
||||
printf("Failed to create videoframe from software bitmap.");
|
||||
printf(" %ws\n", hr.message().c_str());
|
||||
exit(hr.code());
|
||||
}
|
||||
// all done
|
||||
return inputImage;
|
||||
}
|
||||
|
||||
void PrintResults(IVectorView<float> results)
|
||||
{
|
||||
// load the labels
|
||||
auto modulePath = FileHelper::GetModulePath();
|
||||
std::string labelsFilePath =
|
||||
std::string(modulePath.begin(), modulePath.end()) + labelsFileName;
|
||||
labels = FileHelper::LoadLabels(labelsFilePath);
|
||||
|
||||
vector<pair<float, uint32_t>> sortedResults;
|
||||
for (uint32_t i = 0; i < results.Size(); i++) {
|
||||
pair<float, uint32_t> curr;
|
||||
curr.first = results.GetAt(i);
|
||||
curr.second = i;
|
||||
sortedResults.push_back(curr);
|
||||
}
|
||||
std::sort(sortedResults.begin(), sortedResults.end(),
|
||||
[](pair<float, uint32_t> const &a, pair<float, uint32_t> const &b) { return a.first > b.first; });
|
||||
|
||||
// Display the result
|
||||
for (int i = 0; i < 3; i++)
|
||||
{
|
||||
pair<float, uint32_t> curr = sortedResults.at(i);
|
||||
printf("%s with confidence of %f\n", labels[curr.second].c_str(), curr.first);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<packages>
|
||||
<package id="Microsoft.AI.DirectML" version="1.5.1" targetFramework="native" />
|
||||
<package id="Microsoft.AI.MachineLearning" version="1.8.0" targetFramework="native" />
|
||||
<package id="Microsoft.Windows.CppWinRT" version="2.0.210505.3" targetFramework="native" />
|
||||
<package id="Microsoft.AI.MachineLearning" version="1.3.0" targetFramework="native" />
|
||||
<package id="Microsoft.Windows.CppWinRT" version="2.0.200316.3" targetFramework="native" />
|
||||
</packages>
|
||||
|
|
@ -32,9 +32,8 @@
|
|||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.AI.MachineLearning" Version="1.8.0" />
|
||||
<PackageReference Include="Microsoft.Windows.CppWinRT" Version="2.0.210505.3" />
|
||||
<PackageReference Include="Newtonsoft.Json" Version="13.0.1" />
|
||||
<PackageReference Include="Microsoft.AI.MachineLearning" Version="1.7.0" />
|
||||
<PackageReference Include="Newtonsoft.Json" Version="12.0.1" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
|
|
@ -32,8 +32,7 @@
|
|||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Windows.CppWinRT" Version="2.0.210505.3" />
|
||||
<PackageReference Include="Newtonsoft.Json" Version="13.0.1" />
|
||||
<PackageReference Include="Newtonsoft.Json" Version="12.0.1" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
|
|
|
|||
|
|
@ -51,6 +51,7 @@ Global
|
|||
{2CF5F1AA-BF69-498C-8B74-04137B2F67F3}.Release|x86.Build.0 = Release|x86
|
||||
{2CF5F1AA-BF69-498C-8B74-04137B2F67F3}.Release|x86.Deploy.0 = Release|x86
|
||||
{D4357184-82D4-484F-A683-C45F10CC0CD3}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
|
||||
{D4357184-82D4-484F-A683-C45F10CC0CD3}.Debug|Any CPU.Build.0 = Debug|Any CPU
|
||||
{D4357184-82D4-484F-A683-C45F10CC0CD3}.Debug|Any CPU.Deploy.0 = Debug|Any CPU
|
||||
{D4357184-82D4-484F-A683-C45F10CC0CD3}.Debug|ARM.ActiveCfg = Debug|ARM
|
||||
{D4357184-82D4-484F-A683-C45F10CC0CD3}.Debug|ARM.Build.0 = Debug|ARM
|
||||
|
|
@ -88,6 +89,7 @@ Global
|
|||
{2BF804D4-DAA2-42BE-9F21-0E94F021EF53}.Release|x86.ActiveCfg = Release|Win32
|
||||
{2BF804D4-DAA2-42BE-9F21-0E94F021EF53}.Release|x86.Build.0 = Release|Win32
|
||||
{D20A1934-366E-4055-AFFB-3F592FC5744A}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
|
||||
{D20A1934-366E-4055-AFFB-3F592FC5744A}.Debug|Any CPU.Build.0 = Debug|Any CPU
|
||||
{D20A1934-366E-4055-AFFB-3F592FC5744A}.Debug|ARM.ActiveCfg = Debug|Any CPU
|
||||
{D20A1934-366E-4055-AFFB-3F592FC5744A}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{D20A1934-366E-4055-AFFB-3F592FC5744A}.Debug|x64.Build.0 = Debug|x64
|
||||
|
|
@ -101,7 +103,6 @@ Global
|
|||
{D20A1934-366E-4055-AFFB-3F592FC5744A}.Release|x86.ActiveCfg = Release|x86
|
||||
{D20A1934-366E-4055-AFFB-3F592FC5744A}.Release|x86.Build.0 = Release|x86
|
||||
{12103A5B-677A-4286-83D2-54EAB9010C16}.Debug|Any CPU.ActiveCfg = Debug|Win32
|
||||
{12103A5B-677A-4286-83D2-54EAB9010C16}.Debug|Any CPU.Build.0 = Debug|Win32
|
||||
{12103A5B-677A-4286-83D2-54EAB9010C16}.Debug|ARM.ActiveCfg = Debug|Win32
|
||||
{12103A5B-677A-4286-83D2-54EAB9010C16}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{12103A5B-677A-4286-83D2-54EAB9010C16}.Debug|x64.Build.0 = Debug|x64
|
||||
|
|
@ -114,6 +115,7 @@ Global
|
|||
{12103A5B-677A-4286-83D2-54EAB9010C16}.Release|x86.ActiveCfg = Release|Win32
|
||||
{12103A5B-677A-4286-83D2-54EAB9010C16}.Release|x86.Build.0 = Release|Win32
|
||||
{C475A99E-5976-4C5C-941F-1CE3E71F035C}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
|
||||
{C475A99E-5976-4C5C-941F-1CE3E71F035C}.Debug|Any CPU.Build.0 = Debug|Any CPU
|
||||
{C475A99E-5976-4C5C-941F-1CE3E71F035C}.Debug|ARM.ActiveCfg = Debug|Any CPU
|
||||
{C475A99E-5976-4C5C-941F-1CE3E71F035C}.Debug|ARM.Build.0 = Debug|Any CPU
|
||||
{C475A99E-5976-4C5C-941F-1CE3E71F035C}.Debug|x64.ActiveCfg = Debug|Any CPU
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@
|
|||
<AssemblyName>SqueezeNetObjectDetectionCS</AssemblyName>
|
||||
<DefaultLanguage>en-US</DefaultLanguage>
|
||||
<TargetPlatformIdentifier>UAP</TargetPlatformIdentifier>
|
||||
<TargetPlatformVersion Condition=" '$(TargetPlatformVersion)' == '' ">10.0.18362.0</TargetPlatformVersion>
|
||||
<TargetPlatformVersion Condition=" '$(TargetPlatformVersion)' == '' ">10.0.17763.0</TargetPlatformVersion>
|
||||
<TargetPlatformMinVersion>10.0.17763.0</TargetPlatformMinVersion>
|
||||
<MinimumVisualStudioVersion>14</MinimumVisualStudioVersion>
|
||||
<FileAlignment>512</FileAlignment>
|
||||
|
|
@ -137,13 +137,10 @@
|
|||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.NETCore.UniversalWindowsPlatform">
|
||||
<Version>6.2.12</Version>
|
||||
</PackageReference>
|
||||
<PackageReference Include="Microsoft.Windows.CppWinRT">
|
||||
<Version>2.0.210505.3</Version>
|
||||
<Version>6.0.1</Version>
|
||||
</PackageReference>
|
||||
<PackageReference Include="Newtonsoft.Json">
|
||||
<Version>13.0.1</Version>
|
||||
<Version>11.0.1</Version>
|
||||
</PackageReference>
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче