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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/IRuntime.hpp>
#include <armnnTestUtils/TensorHelpers.hpp>
#include <Network.hpp>
#include <VerificationHelpers.hpp>
#include <doctest/doctest.h>
#include <fmt/format.h>
#include <iomanip>
#include <string>
namespace armnnUtils
{
template<typename TParser>
struct ParserPrototxtFixture
{
ParserPrototxtFixture()
: m_Parser(TParser::Create())
, m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions()))
, m_NetworkIdentifier(-1)
{
}
/// 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 SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape,
const armnn::TensorShape& outputTensorShape,
const std::string& inputName,
const std::string& outputName);
void Setup(const std::map<std::string, armnn::TensorShape>& inputShapes,
const std::vector<std::string>& requestedOutputs);
void Setup(const std::map<std::string, armnn::TensorShape>& inputShapes);
void Setup();
armnn::IOptimizedNetworkPtr SetupOptimizedNetwork(
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 that 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 tensor and checks the result against the given output tensor.
/// Calls RunTest with output type of uint8_t for checking comparison operators.
template <std::size_t NumOutputDimensions>
void RunComparisonTest(const std::map<std::string, std::vector<float>>& inputData,
const std::map<std::string, std::vector<uint8_t>>& 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, typename T = float>
void RunTest(const std::map<std::string, std::vector<float>>& inputData,
const std::map<std::string, std::vector<T>>& expectedOutputData);
std::string m_Prototext;
std::unique_ptr<TParser, void(*)(TParser* parser)> m_Parser;
armnn::IRuntimePtr m_Runtime;
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;
/// @}
/// This will store the output shape so it don't need to be passed to the single-input-single-output overload
/// of RunTest().
armnn::TensorShape m_SingleOutputShape;
};
template<typename TParser>
void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const std::string& inputName,
const std::string& outputName)
{
// Stores 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)
{
// Stores 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>::SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape,
const armnn::TensorShape& outputTensorShape,
const std::string& inputName,
const std::string& outputName)
{
// Stores the input name, the output name and the output tensor shape
// so they don't need to be passed to the single-input-single-output RunTest().
m_SingleInputName = inputName;
m_SingleOutputName = outputName;
m_SingleOutputShape = outputTensorShape;
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)
{
std::string errorMessage;
armnn::INetworkPtr network =
m_Parser->CreateNetworkFromString(m_Prototext.c_str(), inputShapes, requestedOutputs);
auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec());
armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);
if (ret != armnn::Status::Success)
{
throw armnn::Exception(fmt::format("LoadNetwork failed with error: '{0}' {1}",
errorMessage,
CHECK_LOCATION().AsString()));
}
}
template<typename TParser>
void ParserPrototxtFixture<TParser>::Setup(const std::map<std::string, armnn::TensorShape>& inputShapes)
{
std::string errorMessage;
armnn::INetworkPtr network =
m_Parser->CreateNetworkFromString(m_Prototext.c_str(), inputShapes);
auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec());
armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);
if (ret != armnn::Status::Success)
{
throw armnn::Exception(fmt::format("LoadNetwork failed with error: '{0}' {1}",
errorMessage,
CHECK_LOCATION().AsString()));
}
}
template<typename TParser>
void ParserPrototxtFixture<TParser>::Setup()
{
std::string errorMessage;
armnn::INetworkPtr network =
m_Parser->CreateNetworkFromString(m_Prototext.c_str());
auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec());
armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);
if (ret != armnn::Status::Success)
{
throw armnn::Exception(fmt::format("LoadNetwork failed with error: '{0}' {1}",
errorMessage,
CHECK_LOCATION().AsString()));
}
}
template<typename TParser>
armnn::IOptimizedNetworkPtr ParserPrototxtFixture<TParser>::SetupOptimizedNetwork(
const std::map<std::string,armnn::TensorShape>& inputShapes,
const std::vector<std::string>& requestedOutputs)
{
armnn::INetworkPtr network =
m_Parser->CreateNetworkFromString(m_Prototext.c_str(), inputShapes, requestedOutputs);
auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec());
return optimized;
}
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>::RunComparisonTest(const std::map<std::string, std::vector<float>>& inputData,
const std::map<std::string, std::vector<uint8_t>>&
expectedOutputData)
{
RunTest<NumOutputDimensions, uint8_t>(inputData, expectedOutputData);
}
template<typename TParser>
template <std::size_t NumOutputDimensions, typename T>
void ParserPrototxtFixture<TParser>::RunTest(const std::map<std::string, std::vector<float>>& inputData,
const std::map<std::string, std::vector<T>>& expectedOutputData)
{
// Sets up the armnn input tensors from the given vectors.
armnn::InputTensors inputTensors;
for (auto&& it : inputData)
{
armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(it.first);
bindingInfo.second.SetConstant(true);
inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
if (bindingInfo.second.GetNumElements() != it.second.size())
{
throw armnn::Exception(fmt::format("Input tensor {0} is expected to have {1} elements. "
"{2} elements supplied. {3}",
it.first,
bindingInfo.second.GetNumElements(),
it.second.size(),
CHECK_LOCATION().AsString()));
}
}
// Allocates storage for the output tensors to be written to and sets up the armnn output tensors.
std::map<std::string, std::vector<T>> outputStorage;
armnn::OutputTensors outputTensors;
for (auto&& it : expectedOutputData)
{
armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first);
outputStorage.emplace(it.first, std::vector<T>(bindingInfo.second.GetNumElements()));
outputTensors.push_back(
{ bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) });
}
m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);
// Compares each output tensor to the expected values.
for (auto&& it : expectedOutputData)
{
armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first);
if (bindingInfo.second.GetNumElements() != it.second.size())
{
throw armnn::Exception(fmt::format("Output tensor {0} is expected to have {1} elements. "
"{2} elements supplied. {3}",
it.first,
bindingInfo.second.GetNumElements(),
it.second.size(),
CHECK_LOCATION().AsString()));
}
// If the expected output shape is set, the output tensor checks will be carried out.
if (m_SingleOutputShape.GetNumDimensions() != 0)
{
if (bindingInfo.second.GetShape().GetNumDimensions() == NumOutputDimensions &&
bindingInfo.second.GetShape().GetNumDimensions() == m_SingleOutputShape.GetNumDimensions())
{
for (unsigned int i = 0; i < m_SingleOutputShape.GetNumDimensions(); ++i)
{
if (m_SingleOutputShape[i] != bindingInfo.second.GetShape()[i])
{
// This exception message could not be created by fmt:format because of an oddity in
// the operator << of TensorShape.
std::stringstream message;
message << "Output tensor " << it.first << " is expected to have "
<< bindingInfo.second.GetShape() << "shape. "
<< m_SingleOutputShape << " shape supplied. "
<< CHECK_LOCATION().AsString();
throw armnn::Exception(message.str());
}
}
}
else
{
throw armnn::Exception(fmt::format("Output tensor {0} is expected to have {1} dimensions. "
"{2} dimensions supplied. {3}",
it.first,
bindingInfo.second.GetShape().GetNumDimensions(),
NumOutputDimensions,
CHECK_LOCATION().AsString()));
}
}
auto outputExpected = it.second;
auto shape = bindingInfo.second.GetShape();
if (std::is_same<T, uint8_t>::value)
{
auto result = CompareTensors(outputExpected, outputStorage[it.first], shape, shape, true);
CHECK_MESSAGE(result.m_Result, result.m_Message.str());
}
else
{
auto result = CompareTensors(outputExpected, outputStorage[it.first], shape, shape);
CHECK_MESSAGE(result.m_Result, result.m_Message.str());
}
}
}
} // namespace armnnUtils
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