From 867eba59ffd2276086a14f7b2632b390c94392d3 Mon Sep 17 00:00:00 2001
From: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Date: Mon, 3 Feb 2020 12:29:56 +0000
Subject: IVGCVSW-4399 Create Sample Dynamic backend

 * Move IWorkload and WorkloadInfo to include/armnn/backends
 * Add simple sample dynamic backend with addition workload
 * Add sample example to run dynamic backend
 * Unit tests

Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: I0753ce35b8e8a6223a1471388b49246d82438a44
---
 samples/CMakeLists.txt    |  5 +++
 samples/DynamicSample.cpp | 80 +++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 85 insertions(+)
 create mode 100644 samples/DynamicSample.cpp

(limited to 'samples')

diff --git a/samples/CMakeLists.txt b/samples/CMakeLists.txt
index 640d5cd705..5505de0bec 100644
--- a/samples/CMakeLists.txt
+++ b/samples/CMakeLists.txt
@@ -2,3 +2,8 @@ if(BUILD_SAMPLE_APP AND ARMNNREF)
     add_executable(SimpleSample SimpleSample.cpp)
     target_link_libraries(SimpleSample armnn ${CMAKE_THREAD_LIBS_INIT})
 endif()
+
+if(SAMPLE_DYNAMIC_BACKEND)
+    add_executable(DynamicSample DynamicSample.cpp)
+    target_link_libraries(DynamicSample armnn ${CMAKE_THREAD_LIBS_INIT})
+endif()
diff --git a/samples/DynamicSample.cpp b/samples/DynamicSample.cpp
new file mode 100644
index 0000000000..3abe12f309
--- /dev/null
+++ b/samples/DynamicSample.cpp
@@ -0,0 +1,80 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include <armnn/INetwork.hpp>
+#include <armnn/IRuntime.hpp>
+#include <armnn/Utils.hpp>
+#include <armnn/Descriptors.hpp>
+
+#include <iostream>
+
+/// A simple example of using the ArmNN SDK API. In this sample, the users single input number is multiplied by 1.0f
+/// using a fully connected layer with a single neuron to produce an output number that is the same as the input.
+int main()
+{
+    using namespace armnn;
+
+    // Construct ArmNN network
+    armnn::NetworkId networkIdentifier;
+    INetworkPtr myNetwork = INetwork::Create();
+
+    IConnectableLayer* input0 = myNetwork->AddInputLayer(0);
+    IConnectableLayer* input1 = myNetwork->AddInputLayer(1);
+    IConnectableLayer* add    = myNetwork->AddAdditionLayer();
+    IConnectableLayer* output = myNetwork->AddOutputLayer(0);
+
+    input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
+    input1->GetOutputSlot(0).Connect(add->GetInputSlot(1));
+    add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+    TensorInfo tensorInfo(TensorShape({2, 1}), DataType::Float32);
+    input0->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+    input1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+    add->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+    // Create ArmNN runtime
+    IRuntime::CreationOptions options; // default options
+    armnn::IRuntimePtr run(armnn::IRuntime::Create(options));
+
+    // Optimise ArmNN network
+    armnn::IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {"SampleDynamic"}, run->GetDeviceSpec());
+    if (!optNet)
+    {
+        // This shouldn't happen for this simple sample, with reference backend.
+        // But in general usage Optimize could fail if the hardware at runtime cannot
+        // support the model that has been provided.
+        std::cerr << "Error: Failed to optimise the input network." << std::endl;
+        return 1;
+    }
+
+    // Load graph into runtime
+    run->LoadNetwork(networkIdentifier, std::move(optNet));
+
+    // input data
+    std::vector<float> input0Data
+        {
+            5.0f, 3.0f
+        };
+    std::vector<float> input1Data
+        {
+            10.0f, 8.0f
+        };
+    std::vector<float> outputData(2);
+
+    InputTensors inputTensors
+        {
+            {0,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input0Data.data())},
+            {1,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input1Data.data())}
+        };
+    OutputTensors outputTensors
+        {
+            {0,armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}
+        };
+
+    // Execute network
+    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+
+    std::cout << "Addition operator result is {" << outputData[0] << "," << outputData[1] << "}" << std::endl;
+    return 0;
+}
-- 
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