From 03c7ff3f6188240baaeaeb405a357a0c58195fec Mon Sep 17 00:00:00 2001 From: Nikhil Raj Date: Tue, 22 Aug 2023 12:00:04 +0100 Subject: IVGCVSW-7702 Update Doxygen Docu for 23.08 Signed-off-by: Nikhil Raj Change-Id: I357a9f7e47614589327c1ac5d95b6224ff77103d --- latest/_simple_sample_8cpp-example.html | 241 ++++++++++++++++++++++++++++++++ 1 file changed, 241 insertions(+) create mode 100644 latest/_simple_sample_8cpp-example.html (limited to 'latest/_simple_sample_8cpp-example.html') diff --git a/latest/_simple_sample_8cpp-example.html b/latest/_simple_sample_8cpp-example.html new file mode 100644 index 0000000000..ae953973ac --- /dev/null +++ b/latest/_simple_sample_8cpp-example.html @@ -0,0 +1,241 @@ + + + + + + + + +Arm NN: SimpleSample.cpp + + + + + + + + + + + + + + + + +
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SimpleSample.cpp
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This is a very simple example which uses the Arm NN SDK API to create a neural network which consists of nothing else but a single fully connected layer with a single weights value. It's as minimalistic as it can get.

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Note
Most of our users won't use our API to create a network manually. Usually you would use one of our software tools like the TfLite Parser that will translate a TfLite model into Arm NN for you. Still it's a very nice example to see how an Arm NN network is created, optimized and executed.
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(You can find more complex examples using the TfLite Parser in samples/ObjectDetection and samples/SpeechRecognition. And another example using PyArmnn in samples/ImageClassification)

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//
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// Copyright © 2017 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. In this sample, the users single input number is multiplied by 1.0f
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/// using a fully connected layer with a single neuron to produce an output number that is the same as the input.
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int main()
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{
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using namespace armnn;
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float number;
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std::cout << "Please enter a number: " << std::endl;
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std::cin >> number;
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// Turn on logging to standard output
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// This is useful in this sample so that users can learn more about what is going on
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ConfigureLogging(true, false, LogSeverity::Warning);
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// Construct ArmNN network
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NetworkId networkIdentifier;
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INetworkPtr myNetwork = INetwork::Create();
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float weightsData[] = {1.0f}; // Identity
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TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32, 0.0f, 0, true);
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weightsInfo.SetConstant();
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ConstTensor weights(weightsInfo, weightsData);
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// Constant layer that now holds weights data for FullyConnected
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IConnectableLayer* const constantWeightsLayer = myNetwork->AddConstantLayer(weights, "const weights");
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FullyConnectedDescriptor fullyConnectedDesc;
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IConnectableLayer* const fullyConnectedLayer = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc,
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"fully connected");
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IConnectableLayer* InputLayer = myNetwork->AddInputLayer(0);
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IConnectableLayer* OutputLayer = myNetwork->AddOutputLayer(0);
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InputLayer->GetOutputSlot(0).Connect(fullyConnectedLayer->GetInputSlot(0));
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constantWeightsLayer->GetOutputSlot(0).Connect(fullyConnectedLayer->GetInputSlot(1));
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fullyConnectedLayer->GetOutputSlot(0).Connect(OutputLayer->GetInputSlot(0));
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// Create ArmNN runtime
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IRuntime::CreationOptions options; // default options
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IRuntimePtr run = IRuntime::Create(options);
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//Set the tensors in the network.
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TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32);
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InputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
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TensorInfo outputTensorInfo(TensorShape({1, 1}), DataType::Float32);
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fullyConnectedLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
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constantWeightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
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// Optimise ArmNN network
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IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {Compute::CpuRef}, 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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//Creates structures for inputs and outputs.
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std::vector<float> inputData{number};
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std::vector<float> outputData(1);
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inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
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inputTensorInfo.SetConstant(true);
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InputTensors inputTensors{{0, armnn::ConstTensor(inputTensorInfo,
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inputData.data())}};
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OutputTensors outputTensors{{0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0),
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outputData.data())}};
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// Execute network
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run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
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std::cout << "Your number was " << outputData[0] << std::endl;
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return 0;
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}
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armnn::InputLayer
A layer user-provided data can be bound to (e.g. inputs, outputs).
Definition: InputLayer.hpp:13
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armnn::INetworkPtr
std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr
Definition: INetwork.hpp:339
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armnn::IOptimizedNetworkPtr
std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr
Definition: INetwork.hpp:340
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armnn::Tensor
A tensor defined by a TensorInfo (shape and data type) and a mutable backing store.
Definition: Tensor.hpp:319
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armnn::FullyConnectedDescriptor
A FullyConnectedDescriptor for the FullyConnectedLayer.
Definition: Descriptors.hpp:507
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armnn::InputTensors
std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors
Definition: Tensor.hpp:392
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armnn::Compute::CpuRef
@ CpuRef
CPU Execution: Reference C++ kernels.
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armnn::OutputSlot::SetTensorInfo
void SetTensorInfo(const TensorInfo &tensorInfo) override
Definition: Layer.cpp:87
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armnn::ConfigureLogging
void ConfigureLogging(bool printToStandardOutput, bool printToDebugOutput, LogSeverity severity)
Configures the logging behaviour of the ARMNN library.
Definition: Utils.cpp:18
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armnn::Layer::GetOutputSlot
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:339
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armnn::OutputTensors
std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors
Definition: Tensor.hpp:393
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armnn::OutputSlot::Connect
int Connect(InputSlot &destination)
Definition: Layer.cpp:112
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armnn::Layer::GetInputSlot
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:337
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armnn::NetworkId
int NetworkId
Definition: IRuntime.hpp:35
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armnn::IRuntimePtr
std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr
Definition: IRuntime.hpp:41
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armnn::IOutputSlot::SetTensorInfo
virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0
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armnn::OutputLayer
A layer user-provided data can be bound to (e.g. inputs, outputs).
Definition: OutputLayer.hpp:13
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main
int main(int argc, char *argv[])
Definition: ArmnnConverter.cpp:327
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armnn::IOutputSlot::Connect
virtual int Connect(IInputSlot &destination)=0
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armnn::IRuntime::Create
static IRuntimePtr Create(const CreationOptions &options)
Definition: Runtime.cpp:52
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armnn::IConnectableLayer::GetOutputSlot
virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0
Get the const output slot handle by slot index.
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armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
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armnn::IConnectableLayer::GetInputSlot
virtual const IInputSlot & GetInputSlot(unsigned int index) const =0
Get a const input slot handle by slot index.
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armnn::ConstTensor
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:327
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armnn::IConnectableLayer
Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.
Definition: INetwork.hpp:80
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armnn::TensorInfo::SetConstant
void SetConstant(const bool IsConstant=true)
Marks the data corresponding to this tensor info as constant.
Definition: Tensor.cpp:514
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armnn::Optimize
IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptionsOpaque &options=OptimizerOptionsOpaque(), Optional< std::vector< std::string > & > messages=EmptyOptional())
Create an optimized version of the network.
Definition: Network.cpp:2091
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armnn::INetwork::Create
static INetworkPtr Create(const NetworkOptions &networkOptions={})
Definition: Network.cpp:664
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