Merge pull request #456 from microsoft/user/numform/batch_support_to_gallery

Update the BatchSupport README to point to the samples gallery

Author: nums11

Samples/BatchSupport/readme.md

@@ -1,108 +1,3 @@
 # Sample of Using Batch
 
-## Overview
-
-This sample tells how to bind and evaluate batches of input in WinML
-
-## Requirements
-
-- [Visual Studio 2017 - 15.4 or higher](https://developer.microsoft.com/en-us/windows/downloads)
-- [Windows 10 - Build 18362 or higher](https://www.microsoft.com/en-us/software-download/windowsinsiderpreviewiso)
-- [Windows SDK - Build 18362 or higher](https://www.microsoft.com/en-us/software-download/windowsinsiderpreviewSDK)
-- Visual Studio Extension for C++/WinRT
-
-  Do the following to add the C++/WinRT extension in Visual Studio.
-  1. Go to **Tools > Extensions and Updates**. 
-  2. Select **Online** in the left pane and search for "WinRT" using the search box.
-  3. Select the **C++/WinRT** extension, click **Download**, and close Visual Studio. The extension should install automatically.
-  4. When the extension has finished installing, re-open Visual Studio.
-
-
-## Build the sample
-
-1. If you download the samples ZIP, be sure to unzip the entire archive, not just the folder with the sample you want to build.
-2. Start Microsoft Visual Studio 2017 and select **File > Open > Project/Solution**.
-3. Starting in the folder where you unzipped the samples, go to the **Samples** subfolder, then the subfolder for this specific sample (**Sample\BatchSupport**). Double-click the Visual Studio solution file (BatchSupport.sln).
-4. Confirm that the project is pointed to the correct SDK that you installed (e.g. 18362). You can do this by right-clicking the project in the **Solution Explorer**, selecting **Properties**, and modifying the **Windows SDK Version**.
-5. Confirm that you are set for the right configuration and platform (for example: Debug, x64).
-6. Build the solution (**Ctrl+Shift+B**).
-
-## Run the sample
-
-1. Open a Command Prompt (in the Windows 10 search bar, type **cmd** and press **Enter**).
-2. Change the current folder to the folder containing the built EXE (`cd <path-to-exe>`).
-3. Run the executable as shown below. Make sure to replace the install location with what matches yours:
-  ```
-  BatchSupport.exe [fixedBatchSize|freeBatchSize] [TensorFloat|VideoFrame]
-  ```
-4. You should get output similar to the following:
-    ```
-    Loading modelfile 'E:\xianz\Windows-Machine-Learning\Samples\BatchSupport\x64\Debug\SqueezeNet.onnx' on the CPU
-    model file loaded in 906 ticks
-    Binding...
-    Running the model...
-    model run took 31 ticks
-    Result for No.0 input
-    tench, Tinca tinca with confidence of 0.738503
-    Result for No.1 input
-    tabby, tabby cat with confidence of 0.931461
-    Result for No.2 input
-    tench, Tinca tinca with confidence of 0.738503
-    ```
-
-## 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 /> 
-
-## Create Session and Bind Inputs
-Take binding batches of VideoFrame as example:
-
-### 1. Create Session:
-1.1 for fixed batch dimension:
-   ```C++
-    //create a session and binding
-    LearningModelDeviceKind deviceKind = LearningModelDeviceKind::Cpu;
-    LearningModelSession session(model, LearningModelDevice(deviceKind));
-    LearningModelBinding binding(session);
-   ```
-
-1.2 for free-dimensional batch size:
-   ```C++
-    // create a session and binding
-    LearningModelDeviceKind deviceKind = LearningModelDeviceKind::Cpu;
-    LearningModelSessionOptions options;
-    if ("freeBatchSize" == modelType) { 
-        // If the model has free dimentional batch, override the free dimension with batch_size
-        options.BatchSizeOverride(static_cast<uint32_t>(BATCH_SIZE));
-    }
-    LearningModelSession session(model, LearningModelDevice(deviceKind), options);
-    LearningModelBinding binding(session);
-   ```
-
-### 2. Bind Inputs
-```C++
-    // Create Batches of VideoFrame
-    std::vector<VideoFrame> inputVideoFrames = {};
-    for (hstring imageName : imageNames) {
-        auto imagePath = static_cast<hstring>(GetModulePath().c_str()) + imageName;
-        auto imageFrame = LoadImageFile(imagePath);
-        inputVideoFrames.emplace_back(imageFrame);
-    }
-    auto videoFrames = winrt::single_threaded_vector(std::move(inputFrames));
-
-    // Bind
-    binding.Bind(inputFeatureDescriptor.Current().Name(), inputVideoFrames);
-```
-
-### 3. Bind Outputs(optional)
-
-The sample does not bind the output, but you could also bind the output as below:
-```C++
-  auto outputShape = std::vector<int64_t>{BATCH_SIZE, 1000, 1, 1};	
-  auto outputValue = TensorFloat::Create(outputShape);	
-  std::wstring outputDataBindingName =	
-      std::wstring(model.OutputFeatures().First().Current().Name());	
-  binding.Bind(outputDataBindingName, outputValue);
-  SampleHelper::PrintResults(outputValue.GetAsVectorView()); // Print Results
-```
+This sample is currently deprecated. To learn how to perform batched inference with Windows ML please refer to the [Batching Sample](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/Batching) in the Windows ML Samples Gallery.