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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/ArmNN.hpp>
#include <backends/test/QuantizeHelper.hpp>
#include <vector>
namespace
{
using namespace armnn;
template<typename T>
bool ConstantUsageTest(const std::vector<BackendId>& computeDevice,
const TensorInfo& commonTensorInfo,
const std::vector<T>& inputData,
const std::vector<T>& constantData,
const std::vector<T>& expectedOutputData)
{
// Create runtime in which test will run
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
// Builds up the structure of the network.
INetworkPtr net(INetwork::Create());
IConnectableLayer* input = net->AddInputLayer(0);
IConnectableLayer* constant = net->AddConstantLayer(ConstTensor(commonTensorInfo, constantData));
IConnectableLayer* add = net->AddAdditionLayer();
IConnectableLayer* output = net->AddOutputLayer(0);
input->GetOutputSlot(0).Connect(add->GetInputSlot(0));
constant->GetOutputSlot(0).Connect(add->GetInputSlot(1));
add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
// Sets the tensors in the network.
input->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
constant->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
add->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
// optimize the network
IOptimizedNetworkPtr optNet = Optimize(*net, computeDevice, runtime->GetDeviceSpec());
// Loads it into the runtime.
NetworkId netId;
runtime->LoadNetwork(netId, std::move(optNet));
// Creates structures for input & output.
std::vector<T> outputData(inputData.size());
InputTensors inputTensors
{
{0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
};
OutputTensors outputTensors
{
{0, Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
};
// Does the inference.
runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
// Checks the results.
return outputData == expectedOutputData;
}
inline bool ConstantUsageFloat32Test(const std::vector<BackendId>& backends)
{
const TensorInfo commonTensorInfo({ 2, 3 }, DataType::Float32);
return ConstantUsageTest(backends,
commonTensorInfo,
std::vector<float>{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, // Input.
std::vector<float>{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, // Const input.
std::vector<float>{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f } // Expected output.
);
}
inline bool ConstantUsageUint8Test(const std::vector<BackendId>& backends)
{
TensorInfo commonTensorInfo({ 2, 3 }, DataType::QuantisedAsymm8);
const float scale = 0.023529f;
const int8_t offset = -43;
commonTensorInfo.SetQuantizationScale(scale);
commonTensorInfo.SetQuantizationOffset(offset);
return ConstantUsageTest(backends,
commonTensorInfo,
QuantizedVector<uint8_t>(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input.
QuantizedVector<uint8_t>(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input.
QuantizedVector<uint8_t>(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }) // Expected output.
);
}
} // anonymous namespace
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