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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TypeUtils.hpp"
#include <armnn/ArmNN.hpp>
#include <armnn/INetwork.hpp>
#include <backendsCommon/test/QuantizeHelper.hpp>
#include <boost/test/unit_test.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.
);
}
template<typename T>
bool CompareBoolean(T a, T b)
{
return (a == 0 && b == 0) ||(a != 0 && b != 0);
};
template<DataType ArmnnIType, DataType ArmnnOType,
typename TInput = ResolveType<ArmnnIType>, typename TOutput = ResolveType<ArmnnOType>>
void EndToEndLayerTestImpl(INetworkPtr network,
const std::map<int, std::vector<TInput>>& inputTensorData,
const std::map<int, std::vector<TOutput>>& expectedOutputData,
std::vector<BackendId> backends)
{
// Create runtime in which test will run
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
// optimize the network
IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec());
// Loads it into the runtime.
NetworkId netId;
runtime->LoadNetwork(netId, std::move(optNet));
InputTensors inputTensors;
inputTensors.reserve(inputTensorData.size());
for (auto&& it : inputTensorData)
{
inputTensors.push_back({it.first,
ConstTensor(runtime->GetInputTensorInfo(netId, it.first), it.second.data())});
}
OutputTensors outputTensors;
outputTensors.reserve(expectedOutputData.size());
std::map<int, std::vector<TOutput>> outputStorage;
for (auto&& it : expectedOutputData)
{
std::vector<TOutput> out(it.second.size());
outputStorage.emplace(it.first, out);
outputTensors.push_back({it.first,
Tensor(runtime->GetOutputTensorInfo(netId, it.first),
outputStorage.at(it.first).data())});
}
// Does the inference.
runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
// Checks the results.
for (auto&& it : expectedOutputData)
{
std::vector<TOutput> out = outputStorage.at(it.first);
if (ArmnnOType == DataType::Boolean)
{
for (unsigned int i = 0; i < out.size(); ++i)
{
BOOST_TEST(CompareBoolean<TOutput>(it.second[i], out[i]));
}
}
else
{
BOOST_TEST(it.second == out);
}
}
}
} // anonymous namespace
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