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82 lines
2.8 KiB
82 lines
2.8 KiB
// |
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// Copyright © 2020 Arm Ltd. All rights reserved. |
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// SPDX-License-Identifier: MIT |
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// |
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#include <armnn/INetwork.hpp> |
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#include <armnn/IRuntime.hpp> |
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#include <armnn/Utils.hpp> |
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#include <armnn/Descriptors.hpp> |
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#include <iostream> |
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/// A simple example of using the ArmNN SDK API with the standalone sample dynamic backend. |
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/// In this example, an addition layer is used to add 2 input tensors to produce a result output tensor. |
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int main() |
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{ |
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using namespace armnn; |
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// Construct ArmNN network |
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armnn::NetworkId networkIdentifier; |
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INetworkPtr myNetwork = INetwork::Create(); |
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IConnectableLayer* input0 = myNetwork->AddInputLayer(0); |
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IConnectableLayer* input1 = myNetwork->AddInputLayer(1); |
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IConnectableLayer* add = myNetwork->AddAdditionLayer(); |
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IConnectableLayer* output = myNetwork->AddOutputLayer(0); |
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input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
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input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
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add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
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TensorInfo tensorInfo(TensorShape({2, 1}), DataType::Float32); |
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input0->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
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input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
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add->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
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// Create ArmNN runtime |
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IRuntime::CreationOptions options; // default options |
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armnn::IRuntimePtr run(armnn::IRuntime::Create(options)); |
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// Optimise ArmNN network |
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armnn::IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {"SampleDynamic"}, run->GetDeviceSpec()); |
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if (!optNet) |
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{ |
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// This shouldn't happen for this simple sample, with reference backend. |
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// But in general usage Optimize could fail if the hardware at runtime cannot |
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// support the model that has been provided. |
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std::cerr << "Error: Failed to optimise the input network." << std::endl; |
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return 1; |
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} |
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// Load graph into runtime |
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run->LoadNetwork(networkIdentifier, std::move(optNet)); |
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// input data |
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std::vector<float> input0Data |
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{ |
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5.0f, 3.0f |
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}; |
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std::vector<float> input1Data |
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{ |
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10.0f, 8.0f |
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}; |
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std::vector<float> outputData(2); |
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TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0); |
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inputTensorInfo.SetConstant(true); |
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InputTensors inputTensors |
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{ |
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{0,armnn::ConstTensor(inputTensorInfo, input0Data.data())}, |
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{1,armnn::ConstTensor(inputTensorInfo, input1Data.data())} |
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}; |
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OutputTensors outputTensors |
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{ |
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{0,armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())} |
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}; |
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// Execute network |
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run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors); |
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std::cout << "Addition operator result is {" << outputData[0] << "," << outputData[1] << "}" << std::endl; |
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return 0; |
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}
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