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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
#pragma once
#include "armnn/IRuntime.hpp"
#include "test/TensorHelpers.hpp"
#include <string>
// TODO davbec01 (14/05/18) : put these into armnnUtils namespace
template<typename TParser>
struct ParserPrototxtFixture
{
ParserPrototxtFixture()
: m_Parser(TParser::Create())
, m_NetworkIdentifier(-1)
{
m_Runtimes.push_back(armnn::IRuntime::Create(armnn::Compute::CpuRef));
#if ARMCOMPUTENEON_ENABLED
m_Runtimes.push_back(armnn::IRuntime::Create(armnn::Compute::CpuAcc));
#endif
#if ARMCOMPUTECL_ENABLED
m_Runtimes.push_back(armnn::IRuntime::Create(armnn::Compute::GpuAcc));
#endif
}
/// Parses and loads the network defined by the m_Prototext string.
/// @{
void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName);
void SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape,
const std::string& inputName,
const std::string& outputName);
void Setup(const std::map<std::string, armnn::TensorShape>& inputShapes,
const std::vector<std::string>& requestedOutputs);
/// @}
/// Executes the network with the given input tensor and checks the result against the given output tensor.
/// This overload assumes the network has a single input and a single output.
template <std::size_t NumOutputDimensions>
void RunTest(const std::vector<float>& inputData, const std::vector<float>& expectedOutputData);
/// Executes the network with the given input tensors and checks the results against the given output tensors.
/// This overload supports multiple inputs and multiple outputs, identified by name.
template <std::size_t NumOutputDimensions>
void RunTest(const std::map<std::string, std::vector<float>>& inputData,
const std::map<std::string, std::vector<float>>& expectedOutputData);
std::string m_Prototext;
std::unique_ptr<TParser, void(*)(TParser* parser)> m_Parser;
std::vector<armnn::IRuntimePtr> m_Runtimes;
armnn::NetworkId m_NetworkIdentifier;
/// If the single-input-single-output overload of Setup() is called, these will store the input and output name
/// so they don't need to be passed to the single-input-single-output overload of RunTest().
/// @{
std::string m_SingleInputName;
std::string m_SingleOutputName;
/// @}
};
template<typename TParser>
void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const std::string& inputName,
const std::string& outputName)
{
// Store the input and output name so they don't need to be passed to the single-input-single-output RunTest().
m_SingleInputName = inputName;
m_SingleOutputName = outputName;
Setup({ }, { outputName });
}
template<typename TParser>
void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape,
const std::string& inputName,
const std::string& outputName)
{
// Store the input and output name so they don't need to be passed to the single-input-single-output RunTest().
m_SingleInputName = inputName;
m_SingleOutputName = outputName;
Setup({ { inputName, inputTensorShape } }, { outputName });
}
template<typename TParser>
void ParserPrototxtFixture<TParser>::Setup(const std::map<std::string, armnn::TensorShape>& inputShapes,
const std::vector<std::string>& requestedOutputs)
{
for (auto&& runtime : m_Runtimes)
{
armnn::INetworkPtr network =
m_Parser->CreateNetworkFromString(m_Prototext.c_str(), inputShapes, requestedOutputs);
auto optimized = Optimize(*network, runtime->GetDeviceSpec());
armnn::Status ret = runtime->LoadNetwork(m_NetworkIdentifier, move(optimized));
if (ret != armnn::Status::Success)
{
throw armnn::Exception("LoadNetwork failed");
}
}
}
template<typename TParser>
template <std::size_t NumOutputDimensions>
void ParserPrototxtFixture<TParser>::RunTest(const std::vector<float>& inputData,
const std::vector<float>& expectedOutputData)
{
RunTest<NumOutputDimensions>({ { m_SingleInputName, inputData } }, { { m_SingleOutputName, expectedOutputData } });
}
template<typename TParser>
template <std::size_t NumOutputDimensions>
void ParserPrototxtFixture<TParser>::RunTest(const std::map<std::string, std::vector<float>>& inputData,
const std::map<std::string, std::vector<float>>& expectedOutputData)
{
for (auto&& runtime : m_Runtimes)
{
using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>;
// Setup the armnn input tensors from the given vectors.
armnn::InputTensors inputTensors;
for (auto&& it : inputData)
{
BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(it.first);
inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
}
// Allocate storage for the output tensors to be written to and setup the armnn output tensors.
std::map<std::string, boost::multi_array<float, NumOutputDimensions>> outputStorage;
armnn::OutputTensors outputTensors;
for (auto&& it : expectedOutputData)
{
BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first);
outputStorage.emplace(it.first, MakeTensor<float, NumOutputDimensions>(bindingInfo.second));
outputTensors.push_back(
{ bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) });
}
runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);
// Compare each output tensor to the expected values
for (auto&& it : expectedOutputData)
{
BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first);
auto outputExpected = MakeTensor<float, NumOutputDimensions>(bindingInfo.second, it.second);
BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
}
}
}
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