Add Non Winrt sample of using WinML

Samples/SqueezeNetObjectDetection/Desktop/cpp/SqueezeNetObjectDetectionCPPNonWinRT/SqueezeNetObjectDetectionCPPNonWinRT.vcxproj

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+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
+  <ImportGroup Label="ExtensionTargets" />
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Samples/SqueezeNetObjectDetection/Desktop/cpp/SqueezeNetObjectDetectionCPPNonWinRT/SqueezeNetObjectDetectionCPPNonWinRT.vcxproj.filters

@@ -0,0 +1,22 @@
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+      <Extensions>cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx</Extensions>
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Samples/SqueezeNetObjectDetection/Desktop/cpp/SqueezeNetObjectDetectionCPPNonWinRT/main.cpp

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+#include <stdint.h>
+#include <utility>
+#include "Windows.AI.MachineLearning.Native.h"
+#include "Windows.AI.MachineLearning.h"
+#include "raw/windows.ai.machinelearning.h"
+#include "raw/windows.ai.machinelearning.gpu.h"
+#include <string>
+#include <vector>
+#include <numeric>
+
+namespace ml = Windows::AI::MachineLearning;
+
+EXTERN_C IMAGE_DOS_HEADER __ImageBase;
+std::wstring GetModulePath() {
+    std::wstring val;
+    wchar_t modulePath[MAX_PATH] = { 0 };
+    GetModuleFileNameW((HINSTANCE)&__ImageBase, modulePath, _countof(modulePath));
+    wchar_t drive[_MAX_DRIVE];
+    wchar_t dir[_MAX_DIR];
+    wchar_t filename[_MAX_FNAME];
+    wchar_t ext[_MAX_EXT];
+    _wsplitpath_s(modulePath, drive, _MAX_DRIVE, dir, _MAX_DIR, filename,
+        _MAX_FNAME, ext, _MAX_EXT);
+
+    val = drive;
+    val += dir;
+
+    return val;
+}
+
+int main()
+{
+    std::wstring model_path = GetModulePath() + L".\\SqueezeNet.onnx";
+
+    std::unique_ptr<ml::learning_model> model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size());
+    std::unique_ptr<ml::learning_model_device> device = std::make_unique<ml::learning_model_device>();
+
+    const wchar_t input_name[] = L"data_0";
+    const wchar_t output_name[] = L"softmaxout_1";
+
+    std::unique_ptr<ml::learning_model_session> session = std::make_unique<ml::learning_model_session>(*model, *device);
+
+    std::unique_ptr<ml::learning_model_binding> binding = std::make_unique<ml::learning_model_binding>(*session.get());
+
+    auto input_shape = std::vector<ml::tensor_shape_type>{ 1, 3, 224, 224 };
+
+    auto input_data = std::vector<float>(1 * 3 * 224 * 224);
+    auto output_shape = std::vector<ml::tensor_shape_type>{ 1, 1000, 1, 1 };
+
+    std::iota(begin(input_data), end(input_data), 0.f);
+
+    binding->bind<float>(
+        input_name, _countof(input_name) - 1,
+        input_shape.data(), input_shape.size(),
+        input_data.data(), input_data.size());
+
+
+    ml::learning_model_results results = session->evaluate(*binding.get());
+    float* p_buffer = nullptr;
+    size_t buffer_size = 0;
+    bool succeeded = 0 == results.get_output(
+        output_name,
+        _countof(output_name) - 1,
+        reinterpret_cast<void**>(&p_buffer),
+        &buffer_size);
+
+    printf("Windows Machine Learning succeeded: %s\n", succeeded ? "TRUE" : "FALSE");
+}

Samples/SqueezeNetObjectDetection/Desktop/cpp/SqueezeNetObjectDetectionCPPNonWinRT/raw/weak_buffer.h

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+// Copyright 2019 Microsoft Corporation. All rights reserved.
+// Use of this source code is governed by a BSD-style license that can be
+// found in the LICENSE file.
+#ifndef WEAK_BUFFER_H
+#define WEAK_BUFFER_H
+
+#include <wrl.h>
+#include <wrl/client.h>
+
+#include <windows.storage.streams.h>
+#include <robuffer.h>
+
+namespace Windows { namespace AI { namespace MachineLearning { namespace Details {
+
+template <typename T>
+struct weak_buffer
+    : public Microsoft::WRL::RuntimeClass<
+          Microsoft::WRL::RuntimeClassFlags<Microsoft::WRL::WinRtClassicComMix | Microsoft::WRL::InhibitRoOriginateError>,
+          ABI::Windows::Storage::Streams::IBuffer,
+          Windows::Storage::Streams::IBufferByteAccess> {
+private:
+    const T* m_p_begin;
+    const T* m_p_end;
+
+public:
+    HRESULT RuntimeClassInitialize(_In_ const T* p_begin, _In_ const T* p_end) {
+        m_p_begin = p_begin;
+        m_p_end = p_end;
+
+        return S_OK;
+    }
+
+    virtual HRESULT STDMETHODCALLTYPE get_Capacity(
+        UINT32 * value)
+    {
+        if (value == nullptr) {
+            return E_POINTER;
+        }
+
+        *value = static_cast<uint32_t>(m_p_end - m_p_begin) * sizeof(T);
+        return S_OK;
+    }
+
+    virtual HRESULT STDMETHODCALLTYPE get_Length(
+        UINT32 * /*value*/)
+    {
+        return E_NOTIMPL;
+    }
+
+    virtual HRESULT STDMETHODCALLTYPE put_Length(
+        UINT32 /*value*/)
+    {
+        return E_NOTIMPL;
+    }
+
+    STDMETHOD(Buffer)(uint8_t** value)
+    {
+        if (value == nullptr) {
+            return E_POINTER;
+        }
+
+        *value = reinterpret_cast<uint8_t*>(const_cast<T*>(m_p_begin));
+        return S_OK;
+    }
+};
+
+}}}} // namespace Windows::AI::MachineLearning::Details
+
+#endif // WEAK_BUFFER_H