resolve comments

Samples/BatchSupport/BatchSupport/BatchSupport.vcxproj

@@ -112,30 +112,28 @@
     </ClCompile>
   </ItemGroup>
   <ItemGroup>
+    <CopyFileToFolders Include="..\..\..\SharedContent\models\SqueezeNet_batch3.onnx">
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <FileType>Document</FileType>
+    </CopyFileToFolders>
+    <CopyFileToFolders Include="..\..\..\SharedContent\models\SqueezeNet_free.onnx">
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <FileType>Document</FileType>
+    </CopyFileToFolders>
     <None Include="packages.config">
       <SubType>Designer</SubType>
     </None>
-    <CopyFileToFolders Include="SqueezeNet.onnx">
-      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
-      <FileType>Document</FileType>
-    </CopyFileToFolders>
-    <CopyFileToFolders Include="SqueezeNet_free.onnx">
-      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
-      <FileType>Document</FileType>
-    </CopyFileToFolders>
   </ItemGroup>
   <ItemGroup>
-    <CopyFileToFolders Include="kitten_224.png">
+    <CopyFileToFolders Include="..\..\..\SharedContent\media\fish.png">
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+    </CopyFileToFolders>
+    <CopyFileToFolders Include="..\..\..\SharedContent\media\kitten_224.png">
       <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
     </CopyFileToFolders>
   </ItemGroup>
   <ItemGroup>
-    <CopyFileToFolders Include="Labels.txt">
-      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
-    </CopyFileToFolders>
-  </ItemGroup>
-  <ItemGroup>
-    <CopyFileToFolders Include="fish.png">
+    <CopyFileToFolders Include="..\..\SqueezeNetObjectDetection\Desktop\cpp\Labels.txt">
       <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
     </CopyFileToFolders>
   </ItemGroup>

Samples/BatchSupport/BatchSupport/BatchSupport.vcxproj.filters

@@ -40,19 +40,19 @@
     <None Include="packages.config" />
   </ItemGroup>
   <ItemGroup>
-    <CopyFileToFolders Include="kitten_224.png">
+    <CopyFileToFolders Include="..\..\..\SharedContent\media\fish.png">
       <Filter>Resource Files</Filter>
     </CopyFileToFolders>
-    <CopyFileToFolders Include="SqueezeNet.onnx">
+    <CopyFileToFolders Include="..\..\..\SharedContent\media\kitten_224.png">
       <Filter>Resource Files</Filter>
     </CopyFileToFolders>
-    <CopyFileToFolders Include="SqueezeNet_free.onnx">
+    <CopyFileToFolders Include="..\..\..\SharedContent\models\SqueezeNet_batch3.onnx">
       <Filter>Resource Files</Filter>
     </CopyFileToFolders>
-    <CopyFileToFolders Include="Labels.txt">
+    <CopyFileToFolders Include="..\..\..\SharedContent\models\SqueezeNet_free.onnx">
       <Filter>Resource Files</Filter>
     </CopyFileToFolders>
-    <CopyFileToFolders Include="fish.png">
+    <CopyFileToFolders Include="..\..\SqueezeNetObjectDetection\Desktop\cpp\Labels.txt">
       <Filter>Resource Files</Filter>
     </CopyFileToFolders>
   </ItemGroup>

Samples/BatchSupport/BatchSupport/SampleHelper.cpp

@@ -38,7 +38,7 @@ std::wstring GetModulePath() {
 }
 
 std::vector<float>
-SoftwareBitmapToSoftwareTensor(SoftwareBitmap softwareBitmap) {
+SoftwareBitmapToFloatVector(SoftwareBitmap softwareBitmap) {
   /* Manully tensorize from CPU resource, steps:
   1. Get the access to buffer of softwarebitmap
   2. Transform the data in buffer to a vector of float
@@ -69,38 +69,18 @@ SoftwareBitmapToSoftwareTensor(SoftwareBitmap softwareBitmap) {
 
   // 2. Transform the data in buffer to a vector of float
   if (BitmapPixelFormat::Bgra8 == pixelFormat) {
-    for (UINT32 i = 0; i < size; i += 4) {
+    for (uint32_t i = 0; i < size; i += 4) {
       // suppose the model expects BGR image.
       // index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
-      UINT32 pixelInd = i / 4;
+      uint32_t pixelInd = i / 4;
       outputVector[pixelInd] = (float)pData[i];
       outputVector[(height * width) + pixelInd] = (float)pData[i + 1];
       outputVector[(height * width * 2) + pixelInd] = (float)pData[i + 2];
     }
-  } else if (BitmapPixelFormat::Rgba8 == pixelFormat) {
-    for (UINT32 i = 0; i < size; i += 4) {
-      // suppose the model expects BGR image.
-      // index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
-      UINT32 pixelInd = i / 4;
-      outputVector[pixelInd] = (float)pData[i + 2];
-      outputVector[(height * width) + pixelInd] = (float)pData[i + 1];
-      outputVector[(height * width * 2) + pixelInd] = (float)pData[i];
-    }
-  } else if (BitmapPixelFormat::Gray8 == pixelFormat) {
-    for (UINT32 i = 0; i < size; i += 4) {
-      // suppose the model expects BGR image.
-      // index 0 is B, 1 is G, 2 is R, 3 is alpha(dropped).
-      UINT32 pixelInd = i / 4;
-      float red = (float)pData[i + 2];
-      float green = (float)pData[i + 1];
-      float blue = (float)pData[i];
-      float gray = 0.2126f * red + 0.7152f * green + 0.0722f * blue;
-      outputVector[pixelInd] = gray;
-    }
   }
+
   // Pixel Value Normalization can be done at here. We are using the range from
-  // 0-255, but the range can be normilized to 0-1 before we return the
-  // TensorFloat.
+  // 0-255, but the range can be normilized to 0-1 before we return
   return outputVector;
 }
 
@@ -130,10 +110,10 @@ hstring GetModelPath(std::string modelType) {
   hstring modelPath;
   if (modelType == "fixedBatchSize") {
     modelPath =
-        static_cast<hstring>(GetModulePath().c_str()) + L"SqueezeNet_free.onnx";
+        static_cast<hstring>(GetModulePath().c_str()) + L"SqueezeNet.onnx";
   } else {
     modelPath =
-        static_cast<hstring>(GetModulePath().c_str()) + L"SqueezeNet.onnx";
+        static_cast<hstring>(GetModulePath().c_str()) + L"SqueezeNet_free.onnx";
   }
   return modelPath;
 }
@@ -145,9 +125,11 @@ TensorFloat CreateInputTensorFloat() {
     auto imagePath = static_cast<hstring>(GetModulePath().c_str()) + imageName;
     auto imageFrame = LoadImageFile(imagePath);
     std::vector<float> imageVector =
-      SoftwareBitmapToSoftwareTensor(imageFrame.SoftwareBitmap());
+      SoftwareBitmapToFloatVector(imageFrame.SoftwareBitmap());
     inputVector.insert(inputVector.end(), imageVector.begin(), imageVector.end());
   }
+
+  // 224, 224 below are height and width specified in model input.
   auto inputShape = std::vector<int64_t>{ BATCH_SIZE, 3, 224, 224 };
   auto inputValue = TensorFloat::CreateFromIterable(
     inputShape,

Samples/BatchSupport/BatchSupport/SampleHelper.h

@@ -11,7 +11,7 @@ namespace SampleHelper
   std::wstring GetModulePath();
 
   // Convert SoftwareBitmap to std::vector<float>
-  std::vector<float> SoftwareBitmapToSoftwareTensor(
+  std::vector<float> SoftwareBitmapToFloatVector(
     winrt::Windows::Graphics::Imaging::SoftwareBitmap softwareBitmap);
 
   // Load Image File as VideoFrame

Samples/BatchSupport/BatchSupport/main.cpp

@@ -25,25 +25,28 @@ int main(int argc, char *argv[]) {
 
   // did they pass in the args
   if (ParseArgs(argc, argv) == false) {
-    printf("Usage: %s [fixedBatchSize|freeBatchSize] [TensorFloat|VideoFrame] \n", argv[0]);
+    printf("Usage: %s [freeBatchSize|fixedBatchSize] [TensorFloat|VideoFrame] \n", argv[0]);
   }
 
   // load the model
   hstring modelPath = SampleHelper::GetModelPath(modelType);
   printf("Loading modelfile '%ws' on the CPU\n", modelPath.c_str());
-  DWORD ticks = GetTickCount();
   auto model = LearningModel::LoadFromFilePath(modelPath);
-  ticks = GetTickCount() - ticks;
-  printf("model file loaded in %d ticks\n", ticks);
 
   // now create a session and binding
   LearningModelDeviceKind deviceKind = LearningModelDeviceKind::Cpu;
-  LearningModelSessionOptions options;
+  LearningModelSession session = nullptr;
   if ("freeBatchSize" == modelType) { 
-    // If the model has free dimentional batch, override the free dimension with batch_size
+    // If the model has free dimensional batch, override the free dimension with batch_size 
+    // for performance improvement.
+    LearningModelSessionOptions options;
+    printf("Override Batch Size by %d\n", BATCH_SIZE);
     options.BatchSizeOverride(static_cast<uint32_t>(BATCH_SIZE));
+    session = LearningModelSession(model, LearningModelDevice(deviceKind), options);
+  }
+  else {
+    session = LearningModelSession(model, LearningModelDevice(deviceKind));
   }
-  LearningModelSession session(model, LearningModelDevice(deviceKind), options);
   LearningModelBinding binding(session);
 
   // bind the intput image
@@ -69,7 +72,7 @@ int main(int argc, char *argv[]) {
 
   // now run the model
   printf("Running the model...\n");
-  ticks = GetTickCount();
+  DWORD ticks = GetTickCount();
   auto results = session.EvaluateAsync(binding, L"RunId").get();
   ticks = GetTickCount() - ticks;
   printf("model run took %d ticks\n", ticks);

Samples/BatchSupport/BatchSupport/packages.config

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <packages>
-  <package id="Microsoft.Windows.CppWinRT" version="2.0.200203.5" targetFramework="native" />
+  <package id="Microsoft.Windows.CppWinRT" version="1.0.181129.3" targetFramework="native" />
 </packages>

Samples/BatchSupport/readme.md

@@ -53,7 +53,7 @@ This sample tells how to bind and evaluate batches of input in WinML
 ## Prepare Model
 1. Download [WinMLDashboard](https://github.com/microsoft/Windows-Machine-Learning/releases/tag/v0.6.1) 
 2. change the batch dimension of model input and output to a fixed number or -1 (free dimension)
-   <img src='.\forReadMe\fixBatchSize.png' width=400 />  <img src='.\forReadMe\freeBatchSize.png' width=400 /> 
+   <img src='./forReadMe/fixBatchSize.png' width=400 />  <img src='./forReadMe/freeBatchSize.png' width=400 /> 
 
 ## Create Session and Bind Inputs
 Take binding batches of VideoFrame as example: