Update the BatchSupport README to poin to the samples gallery
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# Sample of Using Batch
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## Overview
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This sample tells how to bind and evaluate batches of input in WinML
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## Requirements
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- [Visual Studio 2017 - 15.4 or higher](https://developer.microsoft.com/en-us/windows/downloads)
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- [Windows 10 - Build 18362 or higher](https://www.microsoft.com/en-us/software-download/windowsinsiderpreviewiso)
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- [Windows SDK - Build 18362 or higher](https://www.microsoft.com/en-us/software-download/windowsinsiderpreviewSDK)
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- Visual Studio Extension for C++/WinRT
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Do the following to add the C++/WinRT extension in Visual Studio.
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1. Go to **Tools > Extensions and Updates**.
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2. Select **Online** in the left pane and search for "WinRT" using the search box.
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3. Select the **C++/WinRT** extension, click **Download**, and close Visual Studio. The extension should install automatically.
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4. When the extension has finished installing, re-open Visual Studio.
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## Build the sample
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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.
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2. Start Microsoft Visual Studio 2017 and select **File > Open > Project/Solution**.
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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).
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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**.
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5. Confirm that you are set for the right configuration and platform (for example: Debug, x64).
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6. Build the solution (**Ctrl+Shift+B**).
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## Run the sample
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1. Open a Command Prompt (in the Windows 10 search bar, type **cmd** and press **Enter**).
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2. Change the current folder to the folder containing the built EXE (`cd <path-to-exe>`).
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3. Run the executable as shown below. Make sure to replace the install location with what matches yours:
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```
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BatchSupport.exe [fixedBatchSize|freeBatchSize] [TensorFloat|VideoFrame]
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```
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4. You should get output similar to the following:
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```
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Loading modelfile 'E:\xianz\Windows-Machine-Learning\Samples\BatchSupport\x64\Debug\SqueezeNet.onnx' on the CPU
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model file loaded in 906 ticks
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Binding...
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Running the model...
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model run took 31 ticks
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Result for No.0 input
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tench, Tinca tinca with confidence of 0.738503
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Result for No.1 input
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tabby, tabby cat with confidence of 0.931461
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Result for No.2 input
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tench, Tinca tinca with confidence of 0.738503
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```
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## Prepare Model
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1. Download [WinMLDashboard](https://github.com/microsoft/Windows-Machine-Learning/releases/tag/v0.6.1)
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2. change the batch dimension of model input and output to a fixed number or -1 (free dimension)
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<img src='./forReadMe/fixBatchSize.png' width=400 /> <img src='./forReadMe/freeBatchSize.png' width=400 />
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## Create Session and Bind Inputs
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Take binding batches of VideoFrame as example:
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### 1. Create Session:
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1.1 for fixed batch dimension:
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```C++
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//create a session and binding
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LearningModelDeviceKind deviceKind = LearningModelDeviceKind::Cpu;
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LearningModelSession session(model, LearningModelDevice(deviceKind));
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LearningModelBinding binding(session);
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```
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1.2 for free-dimensional batch size:
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```C++
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// create a session and binding
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LearningModelDeviceKind deviceKind = LearningModelDeviceKind::Cpu;
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LearningModelSessionOptions options;
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if ("freeBatchSize" == modelType) {
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// If the model has free dimentional batch, override the free dimension with batch_size
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options.BatchSizeOverride(static_cast<uint32_t>(BATCH_SIZE));
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}
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LearningModelSession session(model, LearningModelDevice(deviceKind), options);
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LearningModelBinding binding(session);
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```
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### 2. Bind Inputs
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```C++
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// Create Batches of VideoFrame
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std::vector<VideoFrame> inputVideoFrames = {};
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for (hstring imageName : imageNames) {
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auto imagePath = static_cast<hstring>(GetModulePath().c_str()) + imageName;
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auto imageFrame = LoadImageFile(imagePath);
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inputVideoFrames.emplace_back(imageFrame);
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}
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auto videoFrames = winrt::single_threaded_vector(std::move(inputFrames));
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// Bind
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binding.Bind(inputFeatureDescriptor.Current().Name(), inputVideoFrames);
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```
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### 3. Bind Outputs(optional)
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The sample does not bind the output, but you could also bind the output as below:
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```C++
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auto outputShape = std::vector<int64_t>{BATCH_SIZE, 1000, 1, 1};
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auto outputValue = TensorFloat::Create(outputShape);
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std::wstring outputDataBindingName =
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std::wstring(model.OutputFeatures().First().Current().Name());
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binding.Bind(outputDataBindingName, outputValue);
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SampleHelper::PrintResults(outputValue.GetAsVectorView()); // Print Results
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```
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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.
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