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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "SchemaSerialize.hpp"
#include "TensorHelpers.hpp"
#include "flatbuffers/idl.h"
#include "flatbuffers/util.h"
#include <ArmnnSchema_generated.h>
#include <armnn/IRuntime.hpp>
#include <armnnDeserializer/IDeserializer.hpp>
#include <armnn/utility/Assert.hpp>
#include <armnn/utility/IgnoreUnused.hpp>
#include <ResolveType.hpp>
#include <fmt/format.h>
#include <doctest/doctest.h>
#include <vector>
using armnnDeserializer::IDeserializer;
using TensorRawPtr = armnnSerializer::TensorInfo*;
struct ParserFlatbuffersSerializeFixture
{
ParserFlatbuffersSerializeFixture() :
m_Parser(IDeserializer::Create()),
m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())),
m_NetworkIdentifier(-1)
{
}
std::vector<uint8_t> m_GraphBinary;
std::string m_JsonString;
std::unique_ptr<IDeserializer, void (*)(IDeserializer* 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;
void Setup()
{
bool ok = ReadStringToBinary();
if (!ok)
{
throw armnn::Exception("LoadNetwork failed while reading binary input");
}
armnn::INetworkPtr network =
m_Parser->CreateNetworkFromBinary(m_GraphBinary);
if (!network)
{
throw armnn::Exception("The parser failed to create an ArmNN network");
}
auto optimized = Optimize(*network, {armnn::Compute::CpuRef},
m_Runtime->GetDeviceSpec());
std::string errorMessage;
armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);
if (ret != armnn::Status::Success)
{
throw armnn::Exception(fmt::format("The runtime failed to load the network. "
"Error was: {0}. in {1} [{2}:{3}]",
errorMessage,
__func__,
__FILE__,
__LINE__));
}
}
void 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();
}
bool ReadStringToBinary()
{
std::string schemafile(&deserialize_schema_start, &deserialize_schema_end);
// parse schema first, so we can use it to parse the data after
flatbuffers::Parser parser;
bool ok = parser.Parse(schemafile.c_str());
CHECK_MESSAGE(ok, std::string("Failed to parse schema file. Error was: " + parser.error_).c_str());
ok &= parser.Parse(m_JsonString.c_str());
CHECK_MESSAGE(ok, std::string("Failed to parse json input. Error was: " + parser.error_).c_str());
if (!ok)
{
return false;
}
{
const uint8_t* bufferPtr = parser.builder_.GetBufferPointer();
size_t size = static_cast<size_t>(parser.builder_.GetSize());
m_GraphBinary.assign(bufferPtr, bufferPtr+size);
}
return ok;
}
/// 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,
armnn::DataType ArmnnType,
typename DataType = armnn::ResolveType<ArmnnType>>
void RunTest(unsigned int layersId,
const std::vector<DataType>& inputData,
const std::vector<DataType>& expectedOutputData);
template<std::size_t NumOutputDimensions,
armnn::DataType ArmnnInputType,
armnn::DataType ArmnnOutputType,
typename InputDataType = armnn::ResolveType<ArmnnInputType>,
typename OutputDataType = armnn::ResolveType<ArmnnOutputType>>
void RunTest(unsigned int layersId,
const std::vector<InputDataType>& inputData,
const std::vector<OutputDataType>& 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,
armnn::DataType ArmnnType,
typename DataType = armnn::ResolveType<ArmnnType>>
void RunTest(unsigned int layersId,
const std::map<std::string, std::vector<DataType>>& inputData,
const std::map<std::string, std::vector<DataType>>& expectedOutputData);
template<std::size_t NumOutputDimensions,
armnn::DataType ArmnnInputType,
armnn::DataType ArmnnOutputType,
typename InputDataType = armnn::ResolveType<ArmnnInputType>,
typename OutputDataType = armnn::ResolveType<ArmnnOutputType>>
void RunTest(unsigned int layersId,
const std::map<std::string, std::vector<InputDataType>>& inputData,
const std::map<std::string, std::vector<OutputDataType>>& expectedOutputData);
void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape,
armnnSerializer::TensorInfo tensorType, const std::string& name,
const float scale, const int64_t zeroPoint)
{
armnn::IgnoreUnused(name);
CHECK_EQ(shapeSize, tensors->dimensions()->size());
CHECK(std::equal(shape.begin(), shape.end(),
tensors->dimensions()->begin(), tensors->dimensions()->end()));
CHECK_EQ(tensorType.dataType(), tensors->dataType());
CHECK_EQ(scale, tensors->quantizationScale());
CHECK_EQ(zeroPoint, tensors->quantizationOffset());
}
};
template<std::size_t NumOutputDimensions, armnn::DataType ArmnnType, typename DataType>
void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId,
const std::vector<DataType>& inputData,
const std::vector<DataType>& expectedOutputData)
{
RunTest<NumOutputDimensions, ArmnnType, ArmnnType, DataType, DataType>(layersId, inputData, expectedOutputData);
}
template<std::size_t NumOutputDimensions,
armnn::DataType ArmnnInputType,
armnn::DataType ArmnnOutputType,
typename InputDataType,
typename OutputDataType>
void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId,
const std::vector<InputDataType>& inputData,
const std::vector<OutputDataType>& expectedOutputData)
{
RunTest<NumOutputDimensions, ArmnnInputType, ArmnnOutputType>(layersId,
{ { m_SingleInputName, inputData } },
{ { m_SingleOutputName, expectedOutputData } });
}
template<std::size_t NumOutputDimensions, armnn::DataType ArmnnType, typename DataType>
void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId,
const std::map<std::string, std::vector<DataType>>& inputData,
const std::map<std::string, std::vector<DataType>>& expectedOutputData)
{
RunTest<NumOutputDimensions, ArmnnType, ArmnnType, DataType, DataType>(layersId, inputData, expectedOutputData);
}
template<std::size_t NumOutputDimensions,
armnn::DataType ArmnnInputType,
armnn::DataType ArmnnOutputType,
typename InputDataType,
typename OutputDataType>
void ParserFlatbuffersSerializeFixture::RunTest(
unsigned int layersId,
const std::map<std::string, std::vector<InputDataType>>& inputData,
const std::map<std::string, std::vector<OutputDataType>>& expectedOutputData)
{
auto ConvertBindingInfo = [](const armnnDeserializer::BindingPointInfo& bindingInfo)
{
return std::make_pair(bindingInfo.m_BindingId, bindingInfo.m_TensorInfo);
};
// Setup the armnn input tensors from the given vectors.
armnn::InputTensors inputTensors;
for (auto&& it : inputData)
{
armnn::BindingPointInfo bindingInfo = ConvertBindingInfo(
m_Parser->GetNetworkInputBindingInfo(layersId, it.first));
bindingInfo.second.SetConstant(true);
armnn::VerifyTensorInfoDataType(bindingInfo.second, ArmnnInputType);
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, std::vector<OutputDataType>> outputStorage;
armnn::OutputTensors outputTensors;
for (auto&& it : expectedOutputData)
{
armnn::BindingPointInfo bindingInfo = ConvertBindingInfo(
m_Parser->GetNetworkOutputBindingInfo(layersId, it.first));
armnn::VerifyTensorInfoDataType(bindingInfo.second, ArmnnOutputType);
outputStorage.emplace(it.first, std::vector<OutputDataType>(bindingInfo.second.GetNumElements()));
outputTensors.push_back(
{ bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) });
}
m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);
// Compare each output tensor to the expected values
for (auto&& it : expectedOutputData)
{
armnn::BindingPointInfo bindingInfo = ConvertBindingInfo(
m_Parser->GetNetworkOutputBindingInfo(layersId, it.first));
auto outputExpected = it.second;
auto result = CompareTensors(outputExpected, outputStorage[it.first],
bindingInfo.second.GetShape(), bindingInfo.second.GetShape());
CHECK_MESSAGE(result.m_Result, result.m_Message.str());
}
}
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